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7,836,391 | 31 | 32 | 31. The computer-program product of claim 30 , where at least some of the outputted links are not provided with an associated graphic indication. | 31. The computer-program product of claim 30 , where at least some of the outputted links are not provided with an associated graphic indication. 32. The computer-program product of claim 31 , where at least one of those of the outputted links that are not provided with an associated graphic rendering has a rank that is higher than the selected link's rank. | 0.5 |
9,495,957 | 1 | 5 | 1. A system for processing a natural language utterance, the system including one or more processors executing one or more computer program modules which, when executed, cause the one or more processors to: generate a context stack comprising context information that corresponds to a plurality of prior utterances, wherein the context stack includes a plurality of context entries; receive the natural language utterance, wherein the natural language utterance is associated with a command or is associated with a request; determine one or more words of the natural language utterance by performing speech recognition on the natural language utterance; identify, from among the plurality of context entries, one or more context entries that correspond to the one or more words, wherein the context information includes the one or more context entries, wherein identifying the one or more context entries comprises: comparing the plurality of context entries to the one or more words; generating, based on the comparison, one or more rank scores for individual context entries of the plurality of context entries; and identifying, based on the one or more rank scores, the one or more context entries from among the plurality of context entries; and determine, based on the determined one or more words and the context information, the command or the request associated with the natural language utterance. | 1. A system for processing a natural language utterance, the system including one or more processors executing one or more computer program modules which, when executed, cause the one or more processors to: generate a context stack comprising context information that corresponds to a plurality of prior utterances, wherein the context stack includes a plurality of context entries; receive the natural language utterance, wherein the natural language utterance is associated with a command or is associated with a request; determine one or more words of the natural language utterance by performing speech recognition on the natural language utterance; identify, from among the plurality of context entries, one or more context entries that correspond to the one or more words, wherein the context information includes the one or more context entries, wherein identifying the one or more context entries comprises: comparing the plurality of context entries to the one or more words; generating, based on the comparison, one or more rank scores for individual context entries of the plurality of context entries; and identifying, based on the one or more rank scores, the one or more context entries from among the plurality of context entries; and determine, based on the determined one or more words and the context information, the command or the request associated with the natural language utterance. 5. The system of claim 1 , wherein identifying the one or more context entries comprises identifying, from among the plurality of context entries, the one or more context entries that most closely correspond to the one or more words. | 0.690981 |
9,355,181 | 1 | 3 | 1. A method for augmenting search results with a user defined suggestion, comprising: identifying a venue entity associated with a set of search results corresponding to a search query; evaluating user defined data to generate a user defined suggestion associated with the venue entity, the evaluating comprising: extracting a set of candidate user suggestions, corresponding to the venue entity, from a social network; generating a candidate graph comprising one or more nodes connected by one more edges based upon the set of candidate user suggestions such that a first node, representing a first candidate user suggestion, is connected to a second node, representing a second candidate user suggestion, by a first edge based upon the first candidate user suggestion corresponding to the second candidate user suggestion above a correspondence threshold; generating one or more suggestion category clusters based upon the candidate graph, a first suggestion category duster comprising one or more nodes connected by at least one edge; and evaluating the set of candidate user suggestions to generate the user defined suggestion, the evaluating comprising evaluating nodes within the first suggestion category cluster to generate the user defined suggestion as a descriptive summary of candidate user suggestions represented by the evaluated nodes within the first suggestion category cluster; augmenting the set of search results with the user defined suggestion to create an augmented set of search results; and providing the augmented set of search results in response to the search query. | 1. A method for augmenting search results with a user defined suggestion, comprising: identifying a venue entity associated with a set of search results corresponding to a search query; evaluating user defined data to generate a user defined suggestion associated with the venue entity, the evaluating comprising: extracting a set of candidate user suggestions, corresponding to the venue entity, from a social network; generating a candidate graph comprising one or more nodes connected by one more edges based upon the set of candidate user suggestions such that a first node, representing a first candidate user suggestion, is connected to a second node, representing a second candidate user suggestion, by a first edge based upon the first candidate user suggestion corresponding to the second candidate user suggestion above a correspondence threshold; generating one or more suggestion category clusters based upon the candidate graph, a first suggestion category duster comprising one or more nodes connected by at least one edge; and evaluating the set of candidate user suggestions to generate the user defined suggestion, the evaluating comprising evaluating nodes within the first suggestion category cluster to generate the user defined suggestion as a descriptive summary of candidate user suggestions represented by the evaluated nodes within the first suggestion category cluster; augmenting the set of search results with the user defined suggestion to create an augmented set of search results; and providing the augmented set of search results in response to the search query. 3. The method of claim 1 , the user defined suggestion corresponding to a first suggestion category, and the method comprising: augmenting the set of search results with a second user defined suggestion corresponding to the first suggestion category. | 0.599359 |
8,839,107 | 1 | 9 | 1. A method for generating scripts in a computer system, comprising: providing a user interface for enabling a user to perform user actions using said user interface to perform a user task; monitoring said user actions by said computer system; determining system operating environment information in accordance with said user actions; generating a script in accordance with said user actions and said determined system operating environment information for repeating said user task using said script generated in accordance with said determined system operating environment information, wherein said generating a script includes: creating a query using a combination of data representing said user actions and said determined system operating environment information and determining a plurality of previously generated scripts associated with the monitored user actions based on said query, wherein previously generated scripts are stored in a data repository; analyzing said monitored user actions; selecting a most efficient script from the plurality of previously generated scripts; and updating the selected, most efficient script responsive to the monitored user actions. | 1. A method for generating scripts in a computer system, comprising: providing a user interface for enabling a user to perform user actions using said user interface to perform a user task; monitoring said user actions by said computer system; determining system operating environment information in accordance with said user actions; generating a script in accordance with said user actions and said determined system operating environment information for repeating said user task using said script generated in accordance with said determined system operating environment information, wherein said generating a script includes: creating a query using a combination of data representing said user actions and said determined system operating environment information and determining a plurality of previously generated scripts associated with the monitored user actions based on said query, wherein previously generated scripts are stored in a data repository; analyzing said monitored user actions; selecting a most efficient script from the plurality of previously generated scripts; and updating the selected, most efficient script responsive to the monitored user actions. 9. The method of claim 1 , wherein said user actions comprise creating a system resource. | 0.720126 |
8,037,078 | 1 | 3 | 1. A method comprising: selecting, by a processor, a toponym-place pair of a target document that includes a plurality of toponyms corresponding to a plurality of toponym-place pairs, wherein the place of each toponym-place pair identifies a geographical location or region designated by the corresponding toponym; and for the selected toponym-place pair, the method further comprises: generating, by the processor, a confidence value, wherein the confidence value presents a confidence that the toponym of the selected toponym-place pair refers to the place of the selected toponym-place pair, and wherein the confidence value is pre-computed and derived from a statistical observation about a plurality of documents; determining if another toponym is present within the target document that has an associated place that is geographically related to the place referred to by the selected toponym-place pair; and if the another toponym is present within the target document that has an associated place that is geographically related to the place referred to by the selected toponym-place pair, boosting the generated confidence value for the selected toponym-place pair for the target document; and ranking the target document based on the boosted confidence value. | 1. A method comprising: selecting, by a processor, a toponym-place pair of a target document that includes a plurality of toponyms corresponding to a plurality of toponym-place pairs, wherein the place of each toponym-place pair identifies a geographical location or region designated by the corresponding toponym; and for the selected toponym-place pair, the method further comprises: generating, by the processor, a confidence value, wherein the confidence value presents a confidence that the toponym of the selected toponym-place pair refers to the place of the selected toponym-place pair, and wherein the confidence value is pre-computed and derived from a statistical observation about a plurality of documents; determining if another toponym is present within the target document that has an associated place that is geographically related to the place referred to by the selected toponym-place pair; and if the another toponym is present within the target document that has an associated place that is geographically related to the place referred to by the selected toponym-place pair, boosting the generated confidence value for the selected toponym-place pair for the target document; and ranking the target document based on the boosted confidence value. 3. The method of claim 1 , wherein determining if the another toponym is present within the target document that has an associated place that is geographically related to the place referred to by the selected toponym-place pair includes identifying the another toponym based, at least in part, on the another toponym having an associated place that is geographically nearby the place referred to by the selected toponym-place pair. | 0.5 |
8,386,568 | 15 | 16 | 15. The method according to claim 1 , wherein the response is created, for at least one of the at least one other participants, by programmatically evaluating rules to determine whether the at least one designated portion is approved for sending to the non-participant. | 15. The method according to claim 1 , wherein the response is created, for at least one of the at least one other participants, by programmatically evaluating rules to determine whether the at least one designated portion is approved for sending to the non-participant. 16. The method according to claim 15 , wherein the rules specify, as factors influencing the approval, at least one of: an identification of the message author; an identification of at least one selected one of the other participants; and at least one keyword to be searched for in the at least one designated portion. | 0.5 |
8,407,197 | 17 | 18 | 17. The processing system of claim 16 , wherein said classifying and indexing the extracted content comprises classifying and indexing the extracted content according to a taxonomy that is specific to a particular industry, and wherein said storing the extracted content in a database so that the extracted content is searchable comprises storing the extracted content in accordance with said taxonomy. | 17. The processing system of claim 16 , wherein said classifying and indexing the extracted content comprises classifying and indexing the extracted content according to a taxonomy that is specific to a particular industry, and wherein said storing the extracted content in a database so that the extracted content is searchable comprises storing the extracted content in accordance with said taxonomy. 18. The processing system of claim 17 , wherein said particular industry is an industry other than electronic catalogs. | 0.5 |
7,827,172 | 6 | 8 | 6. The method of claim 1 , wherein: each comparison, of the first plurality of comparisons, results in a value; making the first plurality of comparisons results in a plurality of values; and the method further comprising weighting one or more values of the plurality of values based on a weight associated with the corresponding different query. | 6. The method of claim 1 , wherein: each comparison, of the first plurality of comparisons, results in a value; making the first plurality of comparisons results in a plurality of values; and the method further comprising weighting one or more values of the plurality of values based on a weight associated with the corresponding different query. 8. The method of claim 6 , wherein weighting one or more values of the plurality of values includes determining a number of search results that have been generated for the corresponding different query, wherein the corresponding different query is a substring of a summary of each of the search results. | 0.649306 |
9,460,721 | 1 | 3 | 1. A method for electronically recognizing and processing speech comprising: creating a first set of grammar rules; loading the first set of grammar rules into a speech recognizer; receiving a first transmitted audio stream containing a first utterance of speech to be recognized; running a language script in the speech recognizer; comparing language in the first transmitted audio stream to language in the first set of grammar rules to determine whether the language in the first transmitted audio stream matches language in the first set of grammar rules; producing a textual representation of the language of the first transmitted audio stream, using language of one of the grammar rules of the first set of grammar rules, to create consumable data when a match between the language of the first transmitted audio stream and the language of one of the first set of grammar rules is found; transmitting the consumable data to a processor; determining which grammar rule has language that most likely matches the language of the first transmitted audio stream, when multiple possible matches are found; producing a textual representation of the language of the first transmitted audio stream using language of a best matched grammar rule of the first set of grammar rules to create consumable data; transmitting the consumable data to the processor; creating a subsequent set of grammar rules when no match is found between the language of the first transmitted audio stream and the language of the first set of grammar rules; repeating the loading, comparing and determining steps with language of the subsequent set of grammar rules and language of the first transmitted audio stream until a match is found; producing a textual representation of the language of the first transmitted audio stream using language of a best matched grammar rule of the subsequent set of grammar rules, to create consumable data; transmitting the consumable data to the processor; and creating a separate set of grammar rules to recognize and process a second transmitted audio stream containing a second utterance separate and distinct from the first utterance in the first audio stream where the separate set of grammar rules is based on the consumable data transmitted from the first transmitted audio stream and repeating the recognizing and processing speech steps, whereby, results of a current recognition event impacts future recognition events by producing consumable data from the loaded set of grammar rules to determine the next appropriate set of rules. | 1. A method for electronically recognizing and processing speech comprising: creating a first set of grammar rules; loading the first set of grammar rules into a speech recognizer; receiving a first transmitted audio stream containing a first utterance of speech to be recognized; running a language script in the speech recognizer; comparing language in the first transmitted audio stream to language in the first set of grammar rules to determine whether the language in the first transmitted audio stream matches language in the first set of grammar rules; producing a textual representation of the language of the first transmitted audio stream, using language of one of the grammar rules of the first set of grammar rules, to create consumable data when a match between the language of the first transmitted audio stream and the language of one of the first set of grammar rules is found; transmitting the consumable data to a processor; determining which grammar rule has language that most likely matches the language of the first transmitted audio stream, when multiple possible matches are found; producing a textual representation of the language of the first transmitted audio stream using language of a best matched grammar rule of the first set of grammar rules to create consumable data; transmitting the consumable data to the processor; creating a subsequent set of grammar rules when no match is found between the language of the first transmitted audio stream and the language of the first set of grammar rules; repeating the loading, comparing and determining steps with language of the subsequent set of grammar rules and language of the first transmitted audio stream until a match is found; producing a textual representation of the language of the first transmitted audio stream using language of a best matched grammar rule of the subsequent set of grammar rules, to create consumable data; transmitting the consumable data to the processor; and creating a separate set of grammar rules to recognize and process a second transmitted audio stream containing a second utterance separate and distinct from the first utterance in the first audio stream where the separate set of grammar rules is based on the consumable data transmitted from the first transmitted audio stream and repeating the recognizing and processing speech steps, whereby, results of a current recognition event impacts future recognition events by producing consumable data from the loaded set of grammar rules to determine the next appropriate set of rules. 3. The method of claim 1 , wherein the grammar rules define words that are expected to be contained in vocal utterances. | 0.698492 |
8,370,386 | 13 | 15 | 13. A computer programmed to create data mining task templates for utilization in data mining activities, said computer programmed to: receive a task template for discovery of common patterns occurring within data mining events; create example tasks using the task template by interpreting the task template with specific values of variables, wherein interpreting the task template comprises: creating a root element of a task from a root element associated with the task template when the variables are a collection data type; and generating attributes and child nodes of the task using the task root element and the task template root element, wherein to generate attributes and child nodes of the task, said computer is further programmed to: loop through all the attributes of the task template root element; obtain a list of text strings from a template string specified as a value of an attribute for each selected attribute in the task template root element; create attributes using the task root element, an attribute name specified by an attribute in the task template root element, and the obtained list of text strings from a same attribute in the task template root element for each selected attribute in the task template root element; and create child elements and text nodes using the task root element and the task template root element; and refine the task template using results returned from execution of the example tasks. | 13. A computer programmed to create data mining task templates for utilization in data mining activities, said computer programmed to: receive a task template for discovery of common patterns occurring within data mining events; create example tasks using the task template by interpreting the task template with specific values of variables, wherein interpreting the task template comprises: creating a root element of a task from a root element associated with the task template when the variables are a collection data type; and generating attributes and child nodes of the task using the task root element and the task template root element, wherein to generate attributes and child nodes of the task, said computer is further programmed to: loop through all the attributes of the task template root element; obtain a list of text strings from a template string specified as a value of an attribute for each selected attribute in the task template root element; create attributes using the task root element, an attribute name specified by an attribute in the task template root element, and the obtained list of text strings from a same attribute in the task template root element for each selected attribute in the task template root element; and create child elements and text nodes using the task root element and the task template root element; and refine the task template using results returned from execution of the example tasks. 15. The computer programmed according to claim 13 wherein to create child elements and text nodes, said computer is further programmed to: loop through all the child nodes of the task template root element; create a child element into the task root element using the task root element and a task template child element for each child element of the task template root element; obtain a list of text strings from a template string specified as a value of a text child node for each text child node in the task template root element; and create text child nodes using the task root element and the obtained list of text strings for each text child node in the task template root element. | 0.5 |
6,108,444 | 6 | 7 | 6. A method of recognizing handwritten words in scanned documents, comprising the steps of: processing a document containing handwritten words wherein features for word localization are extracted in terms of orientation, skew and intra-word separation from handwritten words contained in said document through basis points taken from a single curve of text lines, and wherein affine coordinates are computed for all features on all curves in a curve group; storing said features in a memory; accessing said features from memory for comparison to handwritten words in a scanned document to recognize words within said scanned document; and grouping text segments of handwritten words for purposes of indexing said documents. | 6. A method of recognizing handwritten words in scanned documents, comprising the steps of: processing a document containing handwritten words wherein features for word localization are extracted in terms of orientation, skew and intra-word separation from handwritten words contained in said document through basis points taken from a single curve of text lines, and wherein affine coordinates are computed for all features on all curves in a curve group; storing said features in a memory; accessing said features from memory for comparison to handwritten words in a scanned document to recognize words within said scanned document; and grouping text segments of handwritten words for purposes of indexing said documents. 7. The method of claim 6 wherein said detection of lines of handwritten text takes into account the changes in appearance of the word under 2D affine transforms, changes in the orientation of the lines of text, overall document skew, changes in word appearance due to occlusions, noise, or intra-word handwriting variations made by a single author. | 0.5 |
9,195,760 | 8 | 16 | 8. A computer program product embodied on a non-transitory computer readable medium, the computer program product including computer code adapted to be executed by a computer to perform a method comprising: receiving, from a user system at a host system having a processor system including at least one processor and a memory system, user input for conducting a search, the user input including one or more input terms; automatically searching, by the processor system, a storage area in the memory system at the host system for stored search strings recorded from prior searches that are similar to the user input, the search strings each having one or more search terms; identifying a subset of the stored search strings that are similar to the user input, as a result of the searching; automatically determining, by the host system, a score for each of the search strings in the subset, the score being a value that indicates an expected likelihood that the user will be interested in the search string, wherein the score for each of the search strings is based on a plurality of factors including: a count of a number of the input terms in the user input that are the same as the one or more the search terms in the search string, a relevancy of a collection of documents found when a search is performed using the search string, and how often users have chosen the search string when suggested by the host system; ranking the search strings in the subset, in accordance with the determined scores; and sending, from the host system to the user system, the search strings in the subset, listed in order of the ranking as search suggestions. | 8. A computer program product embodied on a non-transitory computer readable medium, the computer program product including computer code adapted to be executed by a computer to perform a method comprising: receiving, from a user system at a host system having a processor system including at least one processor and a memory system, user input for conducting a search, the user input including one or more input terms; automatically searching, by the processor system, a storage area in the memory system at the host system for stored search strings recorded from prior searches that are similar to the user input, the search strings each having one or more search terms; identifying a subset of the stored search strings that are similar to the user input, as a result of the searching; automatically determining, by the host system, a score for each of the search strings in the subset, the score being a value that indicates an expected likelihood that the user will be interested in the search string, wherein the score for each of the search strings is based on a plurality of factors including: a count of a number of the input terms in the user input that are the same as the one or more the search terms in the search string, a relevancy of a collection of documents found when a search is performed using the search string, and how often users have chosen the search string when suggested by the host system; ranking the search strings in the subset, in accordance with the determined scores; and sending, from the host system to the user system, the search strings in the subset, listed in order of the ranking as search suggestions. 16. The computer program product of claim 8 , wherein a weight is preconfigured for each of the factors. | 0.904936 |
7,665,063 | 46 | 81 | 46. A method of integrating one or more declarative rules into a procedural computational system supporting object-oriented representation of data, the method comprising the steps of: executing, on a digital data processing system that comprises one or more digital data processors, a procedural sequence that comprises a plurality of procedural instructions, updating, upon execution of a procedural instruction that causes change to one or more data in an object, one or more other data participating in one or more declarative rules so as to maintain relationships imposed by said one or more declarative rules among said data, wherein said one or more other data (i) are those not changed by the execution of that procedural instruction, and (ii) are in said object and/or in other objects, where said rules are defined outside that procedural instruction, wherein updating of said one or more other data is performed by executing said rules on said digital data processing system prior to execution of any other procedural instruction in the sequence. | 46. A method of integrating one or more declarative rules into a procedural computational system supporting object-oriented representation of data, the method comprising the steps of: executing, on a digital data processing system that comprises one or more digital data processors, a procedural sequence that comprises a plurality of procedural instructions, updating, upon execution of a procedural instruction that causes change to one or more data in an object, one or more other data participating in one or more declarative rules so as to maintain relationships imposed by said one or more declarative rules among said data, wherein said one or more other data (i) are those not changed by the execution of that procedural instruction, and (ii) are in said object and/or in other objects, where said rules are defined outside that procedural instruction, wherein updating of said one or more other data is performed by executing said rules on said digital data processing system prior to execution of any other procedural instruction in the sequence. 81. The method of claim 46 , further comprising ascertaining a type of declarative system formed by said one or more declarative rules. | 0.741379 |
8,290,822 | 39 | 40 | 39. A computer-implemented system for efficiently displaying selectable attribute values for a product based on a provided criterion, comprising: at least one memory storage device storing: a set of product records that identify products potentially selectable by a user, wherein the product records comprise product attributes that represent one or more features of the product, wherein the product attributes comprise one or more attribute values; and a set of product configuration rules used to define permissible or impermissible product configurations and attributes of the products, wherein the set of product configuration rules comprises: a non-conditional rule that comprises an action statement that is not dependent upon a conditional statement; a product conditional rule that comprises an action statement dependent upon a conditional statement comprising a product attribute value; and, a customer conditional rule that comprises an action statement dependent upon a conditional statement comprising a customer attribute value; at least one server-side processor programmed to: receive a customer attribute value for a customer attribute; prepare a customized product record for transmission to a customer by: evaluating the non-conditional rule; and evaluating the customer conditional rule using the received customer attribute value as the customer attribute value; and prepare a client-side set of product configuration rules, wherein the client-side set of product configuration rules comprises the product conditional rule; and at least one client-side processor programmed to: receive the client-side set of product configuration rules; receive the customized product record; receive a selection of a product attribute value for a product attribute of the customized product record; evaluate the client-side set of product configuration rules using the received product attribute value as the product attribute value for at least one of the client-side set of product configuration rules; and display a product corresponding to the customized product record having a permissible product attribute value based on the evaluation of the client-side set of product configuration rules. | 39. A computer-implemented system for efficiently displaying selectable attribute values for a product based on a provided criterion, comprising: at least one memory storage device storing: a set of product records that identify products potentially selectable by a user, wherein the product records comprise product attributes that represent one or more features of the product, wherein the product attributes comprise one or more attribute values; and a set of product configuration rules used to define permissible or impermissible product configurations and attributes of the products, wherein the set of product configuration rules comprises: a non-conditional rule that comprises an action statement that is not dependent upon a conditional statement; a product conditional rule that comprises an action statement dependent upon a conditional statement comprising a product attribute value; and, a customer conditional rule that comprises an action statement dependent upon a conditional statement comprising a customer attribute value; at least one server-side processor programmed to: receive a customer attribute value for a customer attribute; prepare a customized product record for transmission to a customer by: evaluating the non-conditional rule; and evaluating the customer conditional rule using the received customer attribute value as the customer attribute value; and prepare a client-side set of product configuration rules, wherein the client-side set of product configuration rules comprises the product conditional rule; and at least one client-side processor programmed to: receive the client-side set of product configuration rules; receive the customized product record; receive a selection of a product attribute value for a product attribute of the customized product record; evaluate the client-side set of product configuration rules using the received product attribute value as the product attribute value for at least one of the client-side set of product configuration rules; and display a product corresponding to the customized product record having a permissible product attribute value based on the evaluation of the client-side set of product configuration rules. 40. The computer-implemented system of claim 39 , wherein: the set of product configuration rules further comprises: a product conditional aggregation rule programmed to perform at least one of an aggregation function or a summation function based on a user selection; and the at least one client-side processor is further programmed to: evaluate the product conditional aggregation rule, wherein the user selection of the product conditional aggregation rule comprises the received selection of the product attribute value for the product attribute of the customized product record; and further display the product corresponding to the customized product record based on the evaluation of the product conditional aggregation rule. | 0.5 |
10,115,168 | 1 | 5 | 1. A method for integrating metadata from applications used for social networking into a customer relationship management (CRM) system, the method comprising: obtaining, from applications used for social networking, metadata associated with users of the applications; analyzing the metadata from the applications to infer opportunities, relationships for mapping clients, structures, and subject matter experts; integrating the opportunities, the relationships for mapping the clients, the structures, and the subject matter experts into a customer relationship management (CRM) system to populate the CRM system; identifying potential customers based on integrated opportunities, relationships for mapping the clients, the structures, and the subject matter experts; and managing interactions with current and target customers based on the integrated opportunities, relationships for mapping the clients, the structures, and the subject matter experts. | 1. A method for integrating metadata from applications used for social networking into a customer relationship management (CRM) system, the method comprising: obtaining, from applications used for social networking, metadata associated with users of the applications; analyzing the metadata from the applications to infer opportunities, relationships for mapping clients, structures, and subject matter experts; integrating the opportunities, the relationships for mapping the clients, the structures, and the subject matter experts into a customer relationship management (CRM) system to populate the CRM system; identifying potential customers based on integrated opportunities, relationships for mapping the clients, the structures, and the subject matter experts; and managing interactions with current and target customers based on the integrated opportunities, relationships for mapping the clients, the structures, and the subject matter experts. 5. The method of claim 1 , further comprising updating the CRM system based on modifications made by the users in the applications. | 0.81339 |
9,507,822 | 1 | 2 | 1. A method at a host organization for optimizing database queries in a database system of the host organization, the database system having one or more processors coupled with memory and including both a relational data store and a non-relational data store therein, the method comprising: operating the database system within the host organization; receiving a request at the host organization, the request specifying data for retrieval from the database system; retrieving, based on the request received at the host organization, one or more locations of the data for retrieval; generating, at the host organization, a database query based on the request, wherein the database query specifies a plurality of data elements for retrieval, the plurality of data elements including one or more data elements residing within the non-relational data store and one or more other data elements residing within the relational data store; optimizing the database query to include at least a sub-query or a pre-query; executing the optimized database query against the database system to retrieve the data; wherein optimizing the database query comprises: (i) identifying via a first sub-query to a table within relational data store, the first portion of the data for retrieval, (ii) identifying via a second sub-query to the non-relational data store, the second portion of the data for retrieval, (iii) identifying a data delta between the first sub-query that identifies the first portion of the data retrieved within the relational data store and the second sub-query that identifies the second portion of the data retrieved within the non-relational data store, and (iv) executing a third sub-query replicating missing data from the relational data store to the non-relational data store according to the data delta; wherein the database query comprises a join operation performed by initiating non-relational database queries on the non-relational data store where one or more foreign key parents are stored in the relational data store; and wherein the join operation comprises a plurality of sub-queries which are generated based on query optimizations available via an optimizer agent of the host organization, the query optimizations selected from the group comprising: a specified ordering for the plurality of sub-queries; a target data store for execution of a corresponding sub-query; one or more pre-query assessments based on the data for retrieval; a replication order from the relational data store to the non-relational data store; and an in-memory join operation specifying at least one or more of the plurality of data elements for retrieval from each of the relational data store and the non-relational data store and placed into memory accessible to the optimizer agent and a corresponding sub-query to retrieve the at least one or more of the plurality of data elements from the memory accessible to the optimizer agent in fulfillment of the request. | 1. A method at a host organization for optimizing database queries in a database system of the host organization, the database system having one or more processors coupled with memory and including both a relational data store and a non-relational data store therein, the method comprising: operating the database system within the host organization; receiving a request at the host organization, the request specifying data for retrieval from the database system; retrieving, based on the request received at the host organization, one or more locations of the data for retrieval; generating, at the host organization, a database query based on the request, wherein the database query specifies a plurality of data elements for retrieval, the plurality of data elements including one or more data elements residing within the non-relational data store and one or more other data elements residing within the relational data store; optimizing the database query to include at least a sub-query or a pre-query; executing the optimized database query against the database system to retrieve the data; wherein optimizing the database query comprises: (i) identifying via a first sub-query to a table within relational data store, the first portion of the data for retrieval, (ii) identifying via a second sub-query to the non-relational data store, the second portion of the data for retrieval, (iii) identifying a data delta between the first sub-query that identifies the first portion of the data retrieved within the relational data store and the second sub-query that identifies the second portion of the data retrieved within the non-relational data store, and (iv) executing a third sub-query replicating missing data from the relational data store to the non-relational data store according to the data delta; wherein the database query comprises a join operation performed by initiating non-relational database queries on the non-relational data store where one or more foreign key parents are stored in the relational data store; and wherein the join operation comprises a plurality of sub-queries which are generated based on query optimizations available via an optimizer agent of the host organization, the query optimizations selected from the group comprising: a specified ordering for the plurality of sub-queries; a target data store for execution of a corresponding sub-query; one or more pre-query assessments based on the data for retrieval; a replication order from the relational data store to the non-relational data store; and an in-memory join operation specifying at least one or more of the plurality of data elements for retrieval from each of the relational data store and the non-relational data store and placed into memory accessible to the optimizer agent and a corresponding sub-query to retrieve the at least one or more of the plurality of data elements from the memory accessible to the optimizer agent in fulfillment of the request. 2. The method of claim 1 , wherein the database system further comprises elements of hardware and software that are shared by a plurality of separate and distinct customer organizations, each of the separate and distinct customer organizations being remotely located from a host organization having the database system executing therein. | 0.866482 |
7,643,732 | 18 | 19 | 18. The apparatus of claim 17 , wherein the controller is configured to control the pickup to record the text subtitle stream, the text subtitle stream including the style segment which includes a data field indicating a number of the at least one set of user control style defined in the style segment for each of the at least one region style. | 18. The apparatus of claim 17 , wherein the controller is configured to control the pickup to record the text subtitle stream, the text subtitle stream including the style segment which includes a data field indicating a number of the at least one set of user control style defined in the style segment for each of the at least one region style. 19. The apparatus of claim 18 , wherein the controller is configured to control the pickup to record the text subtitle stream, the text subtitle stream including the style segment which defines the at least one set of user control style, the number of the at least one set of user control style defined for each of the at least one region style is less than or equal to 25. | 0.5 |
6,047,093 | 1 | 2 | 1. A method for providing an encoded marker relating to original machine-represented characters in a document for use by a machine in scanning a printed version of said document including human-recognizable characters to more accurately recover said original machine-represented characters, comprising the steps of: decimating said original machine-represented characters into a plurality of binary values, each character being assigned a binary value; including said binary values in a marker having machine-readable symbology; printing said marker and human-recognizable characters corresponding to said original machine-represented characters in said printed version of said document. | 1. A method for providing an encoded marker relating to original machine-represented characters in a document for use by a machine in scanning a printed version of said document including human-recognizable characters to more accurately recover said original machine-represented characters, comprising the steps of: decimating said original machine-represented characters into a plurality of binary values, each character being assigned a binary value; including said binary values in a marker having machine-readable symbology; printing said marker and human-recognizable characters corresponding to said original machine-represented characters in said printed version of said document. 2. The method of claim 1 further comprising encoding details of said document layout in said marker. | 0.761905 |
9,304,993 | 17 | 18 | 17. The multiple citation corpus report of claim 14 , wherein multiple citation corpus report comprises citation start data, comprising a document identifier, a start column number, and a start line number. | 17. The multiple citation corpus report of claim 14 , wherein multiple citation corpus report comprises citation start data, comprising a document identifier, a start column number, and a start line number. 18. The multiple citation corpus report of claim 17 , wherein multiple citation corpus report comprises citation end data, comprising an end column number and an end line number. | 0.5 |
8,290,895 | 4 | 5 | 4. The method of claim 3 , wherein the language objects are associated with frequency objects containing frequency values for the language objects. | 4. The method of claim 3 , wherein the language objects are associated with frequency objects containing frequency values for the language objects. 5. The method of claim 4 , wherein the language objects are assigned frequency values higher than objects contained in a generic word list. | 0.5 |
8,024,327 | 6 | 7 | 6. The method according to claim 1 , further comprising the acts of: determining an expected statistical distribution of the at least one identifying characteristic; generating at least one comparison set; and determining a statistical distribution of at least one identifying characteristic for the comparison set. | 6. The method according to claim 1 , further comprising the acts of: determining an expected statistical distribution of the at least one identifying characteristic; generating at least one comparison set; and determining a statistical distribution of at least one identifying characteristic for the comparison set. 7. The method according to claim 6 , wherein the act of generating at least one comparison set includes an act of generating a randomly selected set from a larger group of set members. | 0.5 |
9,288,285 | 13 | 16 | 13. A system of one or more computers configured to perform operations comprising: identifying classified public content stored on a server appliance or a repository that is communicably coupled to the server appliance; identifying private content of a user stored on a client appliance or a repository that is communicably coupled to the client appliance, the client appliance communicably coupled to the server appliance through a network; receiving, from the user, i) a request for a recommendation of content and ii) a selection of a level of a hierarchical structure associated with the request for the recommendation of content; based on the selected level, determining one or more proxy keywords associated with one or more keywords of the request; generating a representative query based on i) the one or more proxy keywords and ii) the request for the recommendation of content; determining, based on the representative query, a portion of the classified public content stored on a server appliance or the repository that is communicably coupled to the server appliance; determining, based on the request, a portion of the private content stored on the client appliance or the repository that is communicably coupled to the client appliance; and preparing, for presentation to the user, the portion of the classified public content based on the representative query and the portion of the private content based on the request for the recommendation of content. | 13. A system of one or more computers configured to perform operations comprising: identifying classified public content stored on a server appliance or a repository that is communicably coupled to the server appliance; identifying private content of a user stored on a client appliance or a repository that is communicably coupled to the client appliance, the client appliance communicably coupled to the server appliance through a network; receiving, from the user, i) a request for a recommendation of content and ii) a selection of a level of a hierarchical structure associated with the request for the recommendation of content; based on the selected level, determining one or more proxy keywords associated with one or more keywords of the request; generating a representative query based on i) the one or more proxy keywords and ii) the request for the recommendation of content; determining, based on the representative query, a portion of the classified public content stored on a server appliance or the repository that is communicably coupled to the server appliance; determining, based on the request, a portion of the private content stored on the client appliance or the repository that is communicably coupled to the client appliance; and preparing, for presentation to the user, the portion of the classified public content based on the representative query and the portion of the private content based on the request for the recommendation of content. 16. The system of claim 13 , wherein at least one of the client appliance, the server appliance, or the network comprises a firewall that restricts unauthorized access between the client appliance and the server appliance. | 0.749436 |
7,707,160 | 26 | 27 | 26. The computer system of claim 24 wherein the knowledge base includes server knowledge identifying which of the computers include specific portions of the factual knowledge thereby facilitating selection of the second computer by the first computer. | 26. The computer system of claim 24 wherein the knowledge base includes server knowledge identifying which of the computers include specific portions of the factual knowledge thereby facilitating selection of the second computer by the first computer. 27. The computer system of claim 26 wherein the second computer is operable to employ the server knowledge to identify at least one alternate computer in response to the request from the first computer. | 0.5 |
7,765,209 | 15 | 16 | 15. The method of claim 11 where the source includes a document to which the blog links. | 15. The method of claim 11 where the source includes a document to which the blog links. 16. The method of claim 15 where the document includes a profile of an author associated with the blog. | 0.5 |
8,676,780 | 12 | 13 | 12. The system of claim 11 , further comprising: a hyperlink code set adapted to provide a hyperlink between an in-text citation within the document and a corresponding citation in a bibliography of citations. | 12. The system of claim 11 , further comprising: a hyperlink code set adapted to provide a hyperlink between an in-text citation within the document and a corresponding citation in a bibliography of citations. 13. The system of claim 12 , wherein a document comprises multiple sections, each section having a separate bibliography of citations, and wherein the in-text citation is linked based on the section of text in which it appears and the bibliography associated with that section. | 0.5 |
9,766,786 | 1 | 5 | 1. A mobile media consumption device comprising: a display device; one or more hardware processors; and one or more computer-readable storage devices storing processor-executable instructions that are executable by the one or more hardware processors to implement a storytelling module configured to: present a visual experience capable of telling a story, the story including an authored series of events and visual context, the authored series of events representing a storyline of the story, the visual context providing context for the storyline, the visual experience including story views and context views, the story views each having at least a portion of a respective event of the authored series of events, the context views each including at least a portion of the visual context for the storyline; display a portion of the visual experience on the display device based on a display view that is initially mapped to the portion of the visual experience; cause the display view to move across the visual experience and map to different portions of the visual experience based on a user input; determine whether the display view corresponds to a story view or a context view based on an amount of the respective event visually included within the display view; and perform one or more operations associated with a progression through the authored series of events based on the determination. | 1. A mobile media consumption device comprising: a display device; one or more hardware processors; and one or more computer-readable storage devices storing processor-executable instructions that are executable by the one or more hardware processors to implement a storytelling module configured to: present a visual experience capable of telling a story, the story including an authored series of events and visual context, the authored series of events representing a storyline of the story, the visual context providing context for the storyline, the visual experience including story views and context views, the story views each having at least a portion of a respective event of the authored series of events, the context views each including at least a portion of the visual context for the storyline; display a portion of the visual experience on the display device based on a display view that is initially mapped to the portion of the visual experience; cause the display view to move across the visual experience and map to different portions of the visual experience based on a user input; determine whether the display view corresponds to a story view or a context view based on an amount of the respective event visually included within the display view; and perform one or more operations associated with a progression through the authored series of events based on the determination. 5. The mobile media consumption device as recited in claim 1 , wherein the authored series of events are human-authored and fixed in both order and number. | 0.851533 |
9,626,969 | 4 | 5 | 4. A non-transitory computer-readable medium encoded with instructions that, when executed by a processor, perform a method in a computing system of generating a personalized transcription from an audio recording, wherein the method is performed by a mobile device in communication with a server, wherein computational resources of the server are greater than computational resources of the mobile device, the method comprising: maintaining a personal vocabulary of words on the mobile device associated with a user, wherein the personal vocabulary is based on personal data associated with the user; receiving, from the server, a first transcription of an audio recording, wherein the first transcription is generated by a server automatic speech recognition (ASR) engine at the server and using an ASR vocabulary associated with a population of users, wherein the first transcription includes a first word list and confidence scores associated with a plurality of words in the first word list, and wherein the first transcription includes both words that the ASR engine identified as most likely spoken as well as alternatives to those words; receiving, from the server, audio data corresponding to at least the portion of the audio recording; generating a second transcription, wherein the second transcription is of the received audio data, wherein the second transcription comprises a second word list and confidence scores associated with a plurality of words in the second word list, and wherein the second transcription is generated by a mobile device ASR engine located on the mobile device using the maintained personal vocabulary and an acoustic model associated with the user of the mobile device; re-scoring the first transcription, the re-scoring comprising: comparing the first transcription with the second transcription, and modifying a confidence score associated with an alternative word in the first word list when the mobile device ASR engine indicates a higher confidence score for the alternative word than the confidence score attributed by server the ASR engine to the alternative word; and generating a final transcription based on the re-scored first transcription, the final transcription including a combination of most likely spoken words identified by the server ASR engine as well as the re-scored alternative words identified by the mobile device ASR engine. | 4. A non-transitory computer-readable medium encoded with instructions that, when executed by a processor, perform a method in a computing system of generating a personalized transcription from an audio recording, wherein the method is performed by a mobile device in communication with a server, wherein computational resources of the server are greater than computational resources of the mobile device, the method comprising: maintaining a personal vocabulary of words on the mobile device associated with a user, wherein the personal vocabulary is based on personal data associated with the user; receiving, from the server, a first transcription of an audio recording, wherein the first transcription is generated by a server automatic speech recognition (ASR) engine at the server and using an ASR vocabulary associated with a population of users, wherein the first transcription includes a first word list and confidence scores associated with a plurality of words in the first word list, and wherein the first transcription includes both words that the ASR engine identified as most likely spoken as well as alternatives to those words; receiving, from the server, audio data corresponding to at least the portion of the audio recording; generating a second transcription, wherein the second transcription is of the received audio data, wherein the second transcription comprises a second word list and confidence scores associated with a plurality of words in the second word list, and wherein the second transcription is generated by a mobile device ASR engine located on the mobile device using the maintained personal vocabulary and an acoustic model associated with the user of the mobile device; re-scoring the first transcription, the re-scoring comprising: comparing the first transcription with the second transcription, and modifying a confidence score associated with an alternative word in the first word list when the mobile device ASR engine indicates a higher confidence score for the alternative word than the confidence score attributed by server the ASR engine to the alternative word; and generating a final transcription based on the re-scored first transcription, the final transcription including a combination of most likely spoken words identified by the server ASR engine as well as the re-scored alternative words identified by the mobile device ASR engine. 5. The non-transitory computer-readable medium of claim 4 , wherein the personal data associated with the user includes data from at least one of an address book of the user, an SMS message sent or received by the user, an email sent or received by the user, a social network of the user, or a website visited by the user. | 0.5 |
6,029,131 | 4 | 6 | 4. A method for generating synthetic speech, comprising; detecting natural timing boundaries in words to be spoken by a synthetic speech system, to produce natural timing intervals; identifying phonemes in said natural timing intervals; assigning first time durations for each of said phonemes; changing a selected first time duration of a selected phoneme to achieve a desired time duration for a selected natural timing interval containing said selected phoneme; and setting a plurality of said natural timing intervals to substantially the same second time duration, a particular phoneme having a computed time duration in response to number of phonemes within said selected natural timing interval and said second time durations; wherein at least said selected first time duration is based upon a predetermined parameter indicative of degree to which said selected first time duration may be adjusted without undesirably degrading speech produced by said system. | 4. A method for generating synthetic speech, comprising; detecting natural timing boundaries in words to be spoken by a synthetic speech system, to produce natural timing intervals; identifying phonemes in said natural timing intervals; assigning first time durations for each of said phonemes; changing a selected first time duration of a selected phoneme to achieve a desired time duration for a selected natural timing interval containing said selected phoneme; and setting a plurality of said natural timing intervals to substantially the same second time duration, a particular phoneme having a computed time duration in response to number of phonemes within said selected natural timing interval and said second time durations; wherein at least said selected first time duration is based upon a predetermined parameter indicative of degree to which said selected first time duration may be adjusted without undesirably degrading speech produced by said system. 6. The method of claim 4 further comprising: selecting each natural timing interval to be a respective interval between two respective stressed phonemes. | 0.5 |
8,391,618 | 1 | 12 | 1. A non-transitory computer-readable storage medium that stores program instructions computer-executable to implement: determining a plurality of semantic category scores for a digital image via application of a corresponding plurality of classifiers; automatically determining a semantic category profile for the image based on the plurality of semantic category scores, wherein the semantic category profile characterizes semantic content of the image, and is useable to perform semantic based operations with respect to the image; and performing a semantic based operation wherein the semantic based operation comprises: a search operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image; or a keyword operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image. | 1. A non-transitory computer-readable storage medium that stores program instructions computer-executable to implement: determining a plurality of semantic category scores for a digital image via application of a corresponding plurality of classifiers; automatically determining a semantic category profile for the image based on the plurality of semantic category scores, wherein the semantic category profile characterizes semantic content of the image, and is useable to perform semantic based operations with respect to the image; and performing a semantic based operation wherein the semantic based operation comprises: a search operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image; or a keyword operation based on a semantic similarity measure for a plurality of semantic category profiles for a plurality of digital images, wherein the plurality of semantic category profiles includes the semantic category profile for the image. 12. The non-transitory computer-readable storage medium of claim 1 , wherein the digital image is comprised in the plurality of digital images, wherein the program instructions are further computer-executable to implement: determining information content of a plurality of semantic categories with respect to the plurality of digital images; and automatically determining a subset of the plurality of semantic categories based on the determined information content, wherein each of the subset of the plurality of semantic categories is operable to partition the plurality of digital images. | 0.7147 |
10,002,131 | 1 | 2 | 1. A non-transitory computer readable storage medium storing instructions that, in response to being executed by a computing device, cause the computing device to perform operations for building a user language model that indicates one or more natural languages for a user associated with a user identifier, the operations comprising: operations for receiving an indication of a set of one or more characteristics associated with the user identifier, wherein at least some of the received characteristics correspond to a specified likelihood that the user is facile with a particular language; operations for combining the specified likelihoods to generate a baseline language prediction; operations for receiving indications of one or more user actions, wherein each user action corresponds to a specified expectation that the user is facile with a particular language; and operations for updating the baseline language prediction to form a current language prediction indicating one or more languages the user is facile with, the updating based on a modification of the baseline language prediction using the specified expectations; wherein, for a selected language of the one or more of the languages which the current language prediction indicates the user is facile with, the language model includes at least a first identifier indicating whether the user can read in the selected language and at least a second identifier, different from the first identifier, indicating whether the user can write in the selected language; and wherein the operations for updating of the baseline language prediction comprise operations for associating one or more user actions with a weight value based on an observed intensity or frequency of the user action. | 1. A non-transitory computer readable storage medium storing instructions that, in response to being executed by a computing device, cause the computing device to perform operations for building a user language model that indicates one or more natural languages for a user associated with a user identifier, the operations comprising: operations for receiving an indication of a set of one or more characteristics associated with the user identifier, wherein at least some of the received characteristics correspond to a specified likelihood that the user is facile with a particular language; operations for combining the specified likelihoods to generate a baseline language prediction; operations for receiving indications of one or more user actions, wherein each user action corresponds to a specified expectation that the user is facile with a particular language; and operations for updating the baseline language prediction to form a current language prediction indicating one or more languages the user is facile with, the updating based on a modification of the baseline language prediction using the specified expectations; wherein, for a selected language of the one or more of the languages which the current language prediction indicates the user is facile with, the language model includes at least a first identifier indicating whether the user can read in the selected language and at least a second identifier, different from the first identifier, indicating whether the user can write in the selected language; and wherein the operations for updating of the baseline language prediction comprise operations for associating one or more user actions with a weight value based on an observed intensity or frequency of the user action. 2. The non-transitory computer readable storage medium of claim 1 , wherein the one or more actions taken by the user comprise one or more of: interacting with media identified as corresponding to the particular language; producing a threshold number of media items identified as being in the particular language; and using a translation service to convert another media item to the particular language. | 0.627542 |
7,966,172 | 9 | 11 | 9. One or more processor readable storage devices having processor readable code embodied on the processor readable storage devices, the processor readable code for programming one or more processors to perform a method comprising: (a) receiving a selection of a command; (b) displaying a natural language expression that includes the command and at least a new changeable field embedded in the displayed natural language expression; (c) receiving an indication of data for the new changeable embedded field from direct interaction with the displayed natural language expression; (d) modifying the natural language expression in response to the received data and displaying the modified natural language expression; (e) displaying an add field indicator and a remove field indicator, the remove field indicator is associated with at least one embedded field displayed in the natural language expression; (f) repeating steps (b)-(e) if the add field indicator is selected; and (g) modifying the natural language expression by removing the at least one embedded field from the natural language expression and displaying the modified natural language expression. | 9. One or more processor readable storage devices having processor readable code embodied on the processor readable storage devices, the processor readable code for programming one or more processors to perform a method comprising: (a) receiving a selection of a command; (b) displaying a natural language expression that includes the command and at least a new changeable field embedded in the displayed natural language expression; (c) receiving an indication of data for the new changeable embedded field from direct interaction with the displayed natural language expression; (d) modifying the natural language expression in response to the received data and displaying the modified natural language expression; (e) displaying an add field indicator and a remove field indicator, the remove field indicator is associated with at least one embedded field displayed in the natural language expression; (f) repeating steps (b)-(e) if the add field indicator is selected; and (g) modifying the natural language expression by removing the at least one embedded field from the natural language expression and displaying the modified natural language expression. 11. One or more processor readable storage devices according to claim 9 , wherein the method further comprises: accessing a set of data, the natural language expression identifies a subset of the set of data; determining a condition of the set of data; and pre-populating at least a portion of the natural language expression based on the determined condition, the pre-populating includes adding the command to the natural language expression, the selection of the command includes accepting the adding of the command to the natural language expression. | 0.751349 |
8,898,171 | 5 | 6 | 5. A computer method for searching non-word-based documents containing data with m-measurements over n-dimensional space, comprising the computer executed steps of: (a) collecting a group of non-word-based documents of same type containing data with m-measurements over n-dimensional space to form a collection of documents; (b) dividing each document in said group of collected documents into a plurality of elements of same type; (c) providing a plurality of non-word-based token patterns wherein said plurality of non-word-based token patterns are pre-defined and not derived from said non-word-based documents; (d) tokenizing said collected non-word-based documents by matching their elements of same type against said plurality of pre-defined non-word-based token patterns to generate a master collection of tokens, and providing a name for each token; (e) searching for non-word-based documents in said collection of documents that have the same tokens as a query token or a combination of query tokens, said query is a part of a non-word-based document, by searching said query token or tokens in said generated master collection of tokens, to provide a plurality of matching documents with respective scores; and (f) displaying matching documents in the order of their matching scores. | 5. A computer method for searching non-word-based documents containing data with m-measurements over n-dimensional space, comprising the computer executed steps of: (a) collecting a group of non-word-based documents of same type containing data with m-measurements over n-dimensional space to form a collection of documents; (b) dividing each document in said group of collected documents into a plurality of elements of same type; (c) providing a plurality of non-word-based token patterns wherein said plurality of non-word-based token patterns are pre-defined and not derived from said non-word-based documents; (d) tokenizing said collected non-word-based documents by matching their elements of same type against said plurality of pre-defined non-word-based token patterns to generate a master collection of tokens, and providing a name for each token; (e) searching for non-word-based documents in said collection of documents that have the same tokens as a query token or a combination of query tokens, said query is a part of a non-word-based document, by searching said query token or tokens in said generated master collection of tokens, to provide a plurality of matching documents with respective scores; and (f) displaying matching documents in the order of their matching scores. 6. The computer method of claim 5 wherein said collection of documents is supplied in a data source. | 0.632353 |
7,865,394 | 22 | 44 | 22. A system for creating and distributing at least five hundred individualized multimedia messages over a computer network using an email sent simultaneously, comprising: (a) a computer operatively connected to said network and executing a programmed sequence of instructions; (b) a recipient information access routing within said programmed sequence of instructions for accessing data about a given intended recipient with unique recipient information for each of at least five hundred recipients; (c) a content repository containing multimedia elements that may be combined to form individualized messages with computer files comprising at least one of text and graphics files, and further comprising at least one of audio and video files; (d) a content management routine within said programmed sequence of instructions for retrieving selected multimedia content from the content repository, wherein the process of selecting multimedia content is responsive to information content regarding the given recipient accessed by the recipient information access routine; (e) a multimedia engine routine within said programmed sequence of instructions, for packaging the multimedia content as an individualized message for delivery to the given recipient; and (f) a delivery routine within said programmed sequence of instructions for delivering the individualized message to each of the given recipients including sending an email message simultaneously to each of at least five hundred recipients and wherein at least some of said individualized multimedia content for said at least five hundred recipients are different from at least some other of said individualized multimedia content. | 22. A system for creating and distributing at least five hundred individualized multimedia messages over a computer network using an email sent simultaneously, comprising: (a) a computer operatively connected to said network and executing a programmed sequence of instructions; (b) a recipient information access routing within said programmed sequence of instructions for accessing data about a given intended recipient with unique recipient information for each of at least five hundred recipients; (c) a content repository containing multimedia elements that may be combined to form individualized messages with computer files comprising at least one of text and graphics files, and further comprising at least one of audio and video files; (d) a content management routine within said programmed sequence of instructions for retrieving selected multimedia content from the content repository, wherein the process of selecting multimedia content is responsive to information content regarding the given recipient accessed by the recipient information access routine; (e) a multimedia engine routine within said programmed sequence of instructions, for packaging the multimedia content as an individualized message for delivery to the given recipient; and (f) a delivery routine within said programmed sequence of instructions for delivering the individualized message to each of the given recipients including sending an email message simultaneously to each of at least five hundred recipients and wherein at least some of said individualized multimedia content for said at least five hundred recipients are different from at least some other of said individualized multimedia content. 44. A system as recited in claim 22 , wherein the content management routine further comprises routines for directing content uploading, and the customization of the content database. | 0.775735 |
5,463,713 | 1 | 5 | 1. An apparatus for synthesizing speech from text, comprising: a language processing section determining an accent environment of each mora of each phrase of the text, said accent environment including a height of an accent of each mora; a basic accent pattern table in which a basic accent pattern has been classified according to an accent environment of the mora, the basic accent pattern including pitch data which has been edited from real voice data according to the accent environment; a basic accent pattern processing section selecting the basic accent pattern of each mora from said basic accent pattern table according to the accent environment and processing the basic accent pattern in a pitch according to the accent environment; a correcting section receiving the basic access pattern in the pitch in said basic accent pattern processing section and correcting the pitch according to the number of moras in each phrase and the position of the moras in the phrase so as to correct the data in the corrected accent component; a phrase pattern processing section determining a phrase component according to the number of moras in each phrase of the accent environment; and a speech synthesizing section synthesizing speech according to an accent control pattern of the text which is obtained by adding the basic accent pattern and the basic phrase pattern. | 1. An apparatus for synthesizing speech from text, comprising: a language processing section determining an accent environment of each mora of each phrase of the text, said accent environment including a height of an accent of each mora; a basic accent pattern table in which a basic accent pattern has been classified according to an accent environment of the mora, the basic accent pattern including pitch data which has been edited from real voice data according to the accent environment; a basic accent pattern processing section selecting the basic accent pattern of each mora from said basic accent pattern table according to the accent environment and processing the basic accent pattern in a pitch according to the accent environment; a correcting section receiving the basic access pattern in the pitch in said basic accent pattern processing section and correcting the pitch according to the number of moras in each phrase and the position of the moras in the phrase so as to correct the data in the corrected accent component; a phrase pattern processing section determining a phrase component according to the number of moras in each phrase of the accent environment; and a speech synthesizing section synthesizing speech according to an accent control pattern of the text which is obtained by adding the basic accent pattern and the basic phrase pattern. 5. An apparatus for synthesizing speech from text as claimed in claim 1, wherein said basic accent pattern table is classified in accordance with the accent environment of each mora and the type of each mora. | 0.507109 |
8,719,733 | 12 | 15 | 12. The system of claim 10 , wherein the processor is configured to determine the searching cost associated with the navigation hierarchical structure diagram includes the processor is configured to: calculate a viewing cost of the navigation hierarchical structure diagram based at least in part on confidence levels of leaf nodes associated with the navigation hierarchical structure diagram and branch positions corresponding to the leaf nodes; calculate a clicking cost of the navigation hierarchical structure diagram based at least in part on confidence levels of the leaf nodes and hierarchical positions corresponding to the leaf nodes; and calculate the searching cost associated with the navigation hierarchical structure diagram based at least in part on the clicking cost and the viewing cost. | 12. The system of claim 10 , wherein the processor is configured to determine the searching cost associated with the navigation hierarchical structure diagram includes the processor is configured to: calculate a viewing cost of the navigation hierarchical structure diagram based at least in part on confidence levels of leaf nodes associated with the navigation hierarchical structure diagram and branch positions corresponding to the leaf nodes; calculate a clicking cost of the navigation hierarchical structure diagram based at least in part on confidence levels of the leaf nodes and hierarchical positions corresponding to the leaf nodes; and calculate the searching cost associated with the navigation hierarchical structure diagram based at least in part on the clicking cost and the viewing cost. 15. The system of claim 12 , wherein the processor is configured to calculate the viewing cost of the navigation hierarchical structure diagram based on at least in part on confidence levels of leaf nodes associated with the navigation hierarchical structure diagram and branch positions corresponding to the leaf nodes, includes the processor being configured to: set a predetermined corresponding viewing cost for a node located in Level 1 of the navigation hierarchical structure diagram, wherein a viewing cost of a node located at a next position in the navigation hierarchical structure diagram corresponds to a higher viewing cost than the corresponding viewing cost for the node located in Level 1 ; and add products of corresponding viewing costs and confidence levels for each node located in Level 1 of the navigation hierarchical structure diagram, wherein the corresponding confidence level of a node located in Level 1 of the navigation hierarchical structure includes a sum of all confidence levels corresponding to child nodes to that node. | 0.5 |
9,582,230 | 10 | 17 | 10. A fill-in form document completion system, comprising: a processing device; an image capturing device; one or more communications hardware components; and a non-transitory computer-readable medium containing programming instructions that are configured to cause the processing device to: receive, from the image capturing device, an image file of a printed form having at least one fill-in field that contains a handwritten symbol within a field boundary, process the image file to identify a fill-in field on the printed form and the handwritten symbol that is contained within the identified fill-in field, communicatively connect the system to a proximate user mobile device via the one or more communications hardware components, access a data file from the proximate user mobile device, wherein the data file comprises a plurality of categories and stored values, retrieve a candidate value that corresponds to the identified handwritten symbol by: identifying, from the data file, a category that corresponds to the handwritten symbol, extracting the stored value for the identified category from the data file, and using the extracted stored value as the candidate value, insert the candidate value in the identified fill-in field, and cause a document generation device to generate a document comprising the form with the selected candidate value displayed in the identified fill-in field. | 10. A fill-in form document completion system, comprising: a processing device; an image capturing device; one or more communications hardware components; and a non-transitory computer-readable medium containing programming instructions that are configured to cause the processing device to: receive, from the image capturing device, an image file of a printed form having at least one fill-in field that contains a handwritten symbol within a field boundary, process the image file to identify a fill-in field on the printed form and the handwritten symbol that is contained within the identified fill-in field, communicatively connect the system to a proximate user mobile device via the one or more communications hardware components, access a data file from the proximate user mobile device, wherein the data file comprises a plurality of categories and stored values, retrieve a candidate value that corresponds to the identified handwritten symbol by: identifying, from the data file, a category that corresponds to the handwritten symbol, extracting the stored value for the identified category from the data file, and using the extracted stored value as the candidate value, insert the candidate value in the identified fill-in field, and cause a document generation device to generate a document comprising the form with the selected candidate value displayed in the identified fill-in field. 17. The system of claim 10 , wherein: the electronic device further comprises a touch-sensitive display device; the system also include programming instructions configured to, after processing the image file and before retrieving the candidate value: cause the touch-sensitive display device to display the printed form, and receive, via the touch-sensitive display device, a user selection of the identified fill-in field so that when retrieving the candidate value that corresponds to the identified handwritten symbol, the processing device does so in response to receiving the user selection of the identified fill-in field. | 0.571038 |
8,699,796 | 1 | 8 | 1. A method of identifying sensitive expressions in images for a language with a large alphabet, the method to be performed using a computer and comprising: extracting an image from a computer-readable message; extracting image character-blocks from the image; predicting characters to which the character-blocks correspond using a multi-class learning model, wherein the multi-class learning model is trained using a derived list of sensitive characters which is a subset of the large alphabet; combining the predicted characters into string text; and searching the string text for matches with a predefined list of sensitive expressions in the language with the large alphabet. | 1. A method of identifying sensitive expressions in images for a language with a large alphabet, the method to be performed using a computer and comprising: extracting an image from a computer-readable message; extracting image character-blocks from the image; predicting characters to which the character-blocks correspond using a multi-class learning model, wherein the multi-class learning model is trained using a derived list of sensitive characters which is a subset of the large alphabet; combining the predicted characters into string text; and searching the string text for matches with a predefined list of sensitive expressions in the language with the large alphabet. 8. The method of claim 1 , wherein said extracting the image character-blocks comprises: gray-scaling the image to generate grayscale pixel data; applying a threshold to the grayscale pixel data to generate binary pixel data; and defining set binary pixels connected to each other as belonging to a same character-block. | 0.5 |
8,370,145 | 1 | 4 | 1. A keyword extracting device, comprising: an audio input section that inputs speech sound of a first speaker and a second speaker, wherein at least a start of the speech sound of the first speaker precedes a start of the speech sound of the second speaker, and the speech sound of the first and second speakers includes speech sound of a preceding speech and speech sound of a subsequent speech; a speech segment determination section that determines respective first and second speech segments respectively corresponding to the first and second speakers in connection with the speech sound so as to separately identify speech sound from the first and second speakers; a speech recognition section that recognizes respective speech sounds of the respective first and second speech segments determined for the first and second speakers; a speech response feature extraction section that detects a speech response feature only from the second speech segment corresponding to the second speaker, the speech response feature indicates a presence of a keyword in the first speech segment corresponding to the first speaker based on the second speech segment as a response to the first speech segment of the first speaker; and a keyword extraction section that extracts the keyword from the first speech segment specified by the speech response feature detected by the speech response feature extraction section, wherein the keyword extraction section extracts, as the keyword, a constituent element at the end of the first speech segment corresponding to the preceding speech prior to the speech response. | 1. A keyword extracting device, comprising: an audio input section that inputs speech sound of a first speaker and a second speaker, wherein at least a start of the speech sound of the first speaker precedes a start of the speech sound of the second speaker, and the speech sound of the first and second speakers includes speech sound of a preceding speech and speech sound of a subsequent speech; a speech segment determination section that determines respective first and second speech segments respectively corresponding to the first and second speakers in connection with the speech sound so as to separately identify speech sound from the first and second speakers; a speech recognition section that recognizes respective speech sounds of the respective first and second speech segments determined for the first and second speakers; a speech response feature extraction section that detects a speech response feature only from the second speech segment corresponding to the second speaker, the speech response feature indicates a presence of a keyword in the first speech segment corresponding to the first speaker based on the second speech segment as a response to the first speech segment of the first speaker; and a keyword extraction section that extracts the keyword from the first speech segment specified by the speech response feature detected by the speech response feature extraction section, wherein the keyword extraction section extracts, as the keyword, a constituent element at the end of the first speech segment corresponding to the preceding speech prior to the speech response. 4. The keyword extracting device according to claim 1 , wherein the speech response feature extraction section detects a functional phrase of a predetermined type the second speech segment corresponding to the subsequent speech; and wherein the keyword extraction section extracts the keyword from the first speech segment corresponding to the preceding speech which precedes the second speech segment, corresponding to the subsequent speech, that includes the detected functional phrase. | 0.646889 |
8,145,639 | 1 | 17 | 1. A computerized method for evaluating a patent document, comprising: in a computer having a processor configured for: (a) introducing one or more patent indices, characterizing different aspects of the patent document, and a Patent Quality (PQ) index, depending on said one or more patent indices; a monetary value of the patent document being a function of said PQ index; (b) the Patent Quality index having a single numerical value, and being varied on a bounded interval for said PQ index having respective PQ min and PQ max values; each patent index having a single numerical value and being defined on a bounded interval for each said patent index having respective minimal and maximal values; and (c) transforming said one or more patent indices into said Patent Quality index according to a deterministic non-linear transformation; said non-linear transformation being continuous, monotonous with respect to each of said patent indices, said non-linear transformation being non-linear with respect to at least one of said patent indices; wherein said patent indices and said non-linear transformation are chosen so as to satisfy the following: tending of any one of said patent indices substantially to a respective minimal value, results in said Patent Quality index tending substantially to one of the following, independent of values of other patent indices: the PQ min ; the PQ max ; and wherein said non-linear transformation has a parameter of non-linearity expressed as a real number, and wherein said non-linear transformation is a single-valued transformation providing a single numerical value for said PQ index for any parameter of non-linearity. | 1. A computerized method for evaluating a patent document, comprising: in a computer having a processor configured for: (a) introducing one or more patent indices, characterizing different aspects of the patent document, and a Patent Quality (PQ) index, depending on said one or more patent indices; a monetary value of the patent document being a function of said PQ index; (b) the Patent Quality index having a single numerical value, and being varied on a bounded interval for said PQ index having respective PQ min and PQ max values; each patent index having a single numerical value and being defined on a bounded interval for each said patent index having respective minimal and maximal values; and (c) transforming said one or more patent indices into said Patent Quality index according to a deterministic non-linear transformation; said non-linear transformation being continuous, monotonous with respect to each of said patent indices, said non-linear transformation being non-linear with respect to at least one of said patent indices; wherein said patent indices and said non-linear transformation are chosen so as to satisfy the following: tending of any one of said patent indices substantially to a respective minimal value, results in said Patent Quality index tending substantially to one of the following, independent of values of other patent indices: the PQ min ; the PQ max ; and wherein said non-linear transformation has a parameter of non-linearity expressed as a real number, and wherein said non-linear transformation is a single-valued transformation providing a single numerical value for said PQ index for any parameter of non-linearity. 17. The method as described in claim 1 , wherein the step (a) comprises introducing the patent indices from the following set of patent indices: technical merit, commercial value, legal strength, inventive merit, and utility. | 0.765136 |
9,405,779 | 7 | 9 | 7. A method, comprising: receiving a first search request relating to information stored in an ontology, wherein the ontology comprises at least one instance and the instance has a name; parsing the first search request to determine if the first search request is an instance based search that comprises all or part of a name of at least a first instance in the ontology; performing a first query of a search index associated with the ontology in response to determining that the first search request is an instance based search; receiving a second search request relating to information stored in the ontology; parsing the second search request to determine if the second search request is an instance based search that comprises all or part of a name of at least a second instance in the ontology; and performing a second query of at least the ontology in response to determining that the second search request is not an instance based search; receiving a third search request relating to information not stored in the ontology; parsing the third search request to determine that the third search request is an instance based search that comprises all or part of a name of at least a third instance in the ontology; performing a third query of the search index and retrieving metadata associated with the third instance from the search index, wherein the metadata comprises information about a data source associated with the third instance; retrieving information from the data source that are not stored in the ontology; and wherein the search index and the ontology queried in parallel, wherein the search index is generated based at least in part upon an unstructured data element by streaming and normalizing received data terms from the data source and the ontology is generated based at least in part upon an structured data element at least one data mitigation and classification rule. | 7. A method, comprising: receiving a first search request relating to information stored in an ontology, wherein the ontology comprises at least one instance and the instance has a name; parsing the first search request to determine if the first search request is an instance based search that comprises all or part of a name of at least a first instance in the ontology; performing a first query of a search index associated with the ontology in response to determining that the first search request is an instance based search; receiving a second search request relating to information stored in the ontology; parsing the second search request to determine if the second search request is an instance based search that comprises all or part of a name of at least a second instance in the ontology; and performing a second query of at least the ontology in response to determining that the second search request is not an instance based search; receiving a third search request relating to information not stored in the ontology; parsing the third search request to determine that the third search request is an instance based search that comprises all or part of a name of at least a third instance in the ontology; performing a third query of the search index and retrieving metadata associated with the third instance from the search index, wherein the metadata comprises information about a data source associated with the third instance; retrieving information from the data source that are not stored in the ontology; and wherein the search index and the ontology queried in parallel, wherein the search index is generated based at least in part upon an unstructured data element by streaming and normalizing received data terms from the data source and the ontology is generated based at least in part upon an structured data element at least one data mitigation and classification rule. 9. The method of claim 7 , wherein performing either the first query or the second query further comprises identifying one or more instances in the ontology based on the search request. | 0.808091 |
8,156,473 | 4 | 5 | 4. A method in accordance with claim 2 , wherein the breakpoint further includes a Java resource line number that defines a particular statement of a Java class of the executing business application. | 4. A method in accordance with claim 2 , wherein the breakpoint further includes a Java resource line number that defines a particular statement of a Java class of the executing business application. 5. A method in accordance with claim 4 , further comprising associating the Java class identifier and the Java resource line number with a model identifier that identifies the model and a model element identifier that identifies a particular element of the model. | 0.5 |
7,561,780 | 10 | 16 | 10. A method for decoding a text subtitle stream recorded on a recording medium, the method comprising: preloading the text subtitle stream into a subtitle preloading buffer at once, the text subtitle stream including a style segment defining region styles and a presentation segment including presentation information and text data for at least one region, the text data including a region style identifier and one or more text strings for each region; preloading related font data into a font preloading buffer at once; storing the style segment in a composition buffer after the text subtitle stream is preloaded, the style segment including rendering information and composition information; parsing the presentation segment into the composition information and the text data for each region; storing the parsed composition information in the composition buffer for each region; storing the parsed text data in a dialog buffer for each region; rendering the text strings into a bitmap object for each region based on the rendering information and the preloaded font data; storing the rendered bitmap object into a bitmap object buffer for each region; and composing the stored bitmap object within a graphics plane for each region according to the composition information. | 10. A method for decoding a text subtitle stream recorded on a recording medium, the method comprising: preloading the text subtitle stream into a subtitle preloading buffer at once, the text subtitle stream including a style segment defining region styles and a presentation segment including presentation information and text data for at least one region, the text data including a region style identifier and one or more text strings for each region; preloading related font data into a font preloading buffer at once; storing the style segment in a composition buffer after the text subtitle stream is preloaded, the style segment including rendering information and composition information; parsing the presentation segment into the composition information and the text data for each region; storing the parsed composition information in the composition buffer for each region; storing the parsed text data in a dialog buffer for each region; rendering the text strings into a bitmap object for each region based on the rendering information and the preloaded font data; storing the rendered bitmap object into a bitmap object buffer for each region; and composing the stored bitmap object within a graphics plane for each region according to the composition information. 16. The method of claim 10 , wherein the composition information includes the presentation information included in the presentation segment. | 0.688889 |
7,494,046 | 6 | 15 | 6. The apparatus according to claim 4 wherein the control system is in the housing and in operative connection with the currency dispenser device, the check imaging device, the at least one stack transport device, and the document unstacking device, and wherein the control system is operative to cause communication with at least one remote computer to cause the account to be credited for at least one document in the stack. | 6. The apparatus according to claim 4 wherein the control system is in the housing and in operative connection with the currency dispenser device, the check imaging device, the at least one stack transport device, and the document unstacking device, and wherein the control system is operative to cause communication with at least one remote computer to cause the account to be credited for at least one document in the stack. 15. The apparatus according to claim 6 wherein the control system is operative responsive to at least one input through the at least one input device to cause at least one currency sheet to be dispensed from the machine through operation of the currency dispenser device and to cause the account to be assessed for the dispense of the at least one currency sheet. | 0.532216 |
9,152,652 | 1 | 3 | 1. A method performed by data processing apparatus, the method comprising: identifying responsive images for a search phrase that includes two or more terms; determining, by one or more processors, interaction rankings for each of the responsive images based on a number of user interactions with the responsive image; creating, by one or more processors, two or more sub-queries based on the search phrase, the sub-queries each being a proper subset of the two or more terms; for each sub-query from the two or more sub-queries: determining, by one or more processors, sub-query model rankings for the responsive images based on a sub-query model for the sub-query and visual features of the responsive images, the sub-query model being an image relevance model for the sub-query; and determining, by one or more processors, a search phrase score for the sub-query model, the search phrase score being based on a measure of similarity between positions of the responsive images in each of the interaction rankings and the sub-query model rankings; and selecting, based on the search phrase scores for the sub-queries, one of the sub-query models as a model for the search phrase, the selected sub-query model having a search phrase score that meets a threshold search phrase score. | 1. A method performed by data processing apparatus, the method comprising: identifying responsive images for a search phrase that includes two or more terms; determining, by one or more processors, interaction rankings for each of the responsive images based on a number of user interactions with the responsive image; creating, by one or more processors, two or more sub-queries based on the search phrase, the sub-queries each being a proper subset of the two or more terms; for each sub-query from the two or more sub-queries: determining, by one or more processors, sub-query model rankings for the responsive images based on a sub-query model for the sub-query and visual features of the responsive images, the sub-query model being an image relevance model for the sub-query; and determining, by one or more processors, a search phrase score for the sub-query model, the search phrase score being based on a measure of similarity between positions of the responsive images in each of the interaction rankings and the sub-query model rankings; and selecting, based on the search phrase scores for the sub-queries, one of the sub-query models as a model for the search phrase, the selected sub-query model having a search phrase score that meets a threshold search phrase score. 3. The method of claim 1 , further comprising creating an interaction histogram based on the interaction rankings and the numbers of user interactions with the responsive images. | 0.853859 |
8,583,466 | 19 | 20 | 19. A system for facilitating a workflow using a workflow template in a call center, comprising: a computer, including a computer readable medium, a processor and a memory coupled to the processor, one or more servers executed from the memory to providing a first workflow template associated with a workflow item, wherein the first workflow template includes data and scripts for rendering the workflow template and one or more routing rules and one or more trigger points defined for said workflow item, wherein said each trigger point specifies an action to be taken with respect to said workflow item, including routing and supervisor intervention, wherein said one or more routing rules include a workgroup-related threshold that triggers a routing action to be taken with respect to the workgroup, and an agent-related threshold that triggers a routing action to be taken with respect to the agent; linking said first workflow template to a project of an interaction type, wherein the interaction type is one of a phone, web callback, email, and chat, wherein the linking is performed by receiving, via a manager application, a first selection of of a project template defining the interaction type from a predefined dropdown list of project templates, wherein the first selection generates a second dropdown list of one or more workflow templates including the first workflow template, and a third dropdown list of one or more additional workflow templates, receiving a second selection of the first workflow template from the second dropdown list, receiving a third selection of a third workflow template from the third dropdown list, receiving a selection of an entry and exit points for each of the workflow templates selected in the second and third selections, and storing attributes of each of the project template and workflow templates selected in the first, second and third selections and attributes of the entry and exit points, wherein each attribute is linked to at least one of a plurality of scripts used to generate logic flows for the project; routing said workflow item according to the one or more routing rules as defined in the first workflow template, via a server running on one or more microprocessors, to at least one of an agent and a knowledge worker; and receiving a score via a survey of a supervisor and the score being based on a key performance indicator (KPI) library linked to the workflow template selected in the second or third selection and selected from a KPI dropdown list associated with said at least one of an agent and a knowledge worker based on the agent's or the knowledge worker's handling of said workflow item associated with the first workflow template in the project, wherein one or more scoring values are stored in a database and are used for subsequent routing decisions. | 19. A system for facilitating a workflow using a workflow template in a call center, comprising: a computer, including a computer readable medium, a processor and a memory coupled to the processor, one or more servers executed from the memory to providing a first workflow template associated with a workflow item, wherein the first workflow template includes data and scripts for rendering the workflow template and one or more routing rules and one or more trigger points defined for said workflow item, wherein said each trigger point specifies an action to be taken with respect to said workflow item, including routing and supervisor intervention, wherein said one or more routing rules include a workgroup-related threshold that triggers a routing action to be taken with respect to the workgroup, and an agent-related threshold that triggers a routing action to be taken with respect to the agent; linking said first workflow template to a project of an interaction type, wherein the interaction type is one of a phone, web callback, email, and chat, wherein the linking is performed by receiving, via a manager application, a first selection of of a project template defining the interaction type from a predefined dropdown list of project templates, wherein the first selection generates a second dropdown list of one or more workflow templates including the first workflow template, and a third dropdown list of one or more additional workflow templates, receiving a second selection of the first workflow template from the second dropdown list, receiving a third selection of a third workflow template from the third dropdown list, receiving a selection of an entry and exit points for each of the workflow templates selected in the second and third selections, and storing attributes of each of the project template and workflow templates selected in the first, second and third selections and attributes of the entry and exit points, wherein each attribute is linked to at least one of a plurality of scripts used to generate logic flows for the project; routing said workflow item according to the one or more routing rules as defined in the first workflow template, via a server running on one or more microprocessors, to at least one of an agent and a knowledge worker; and receiving a score via a survey of a supervisor and the score being based on a key performance indicator (KPI) library linked to the workflow template selected in the second or third selection and selected from a KPI dropdown list associated with said at least one of an agent and a knowledge worker based on the agent's or the knowledge worker's handling of said workflow item associated with the first workflow template in the project, wherein one or more scoring values are stored in a database and are used for subsequent routing decisions. 20. The system of claim 19 , wherein defining the one or more trigger points includes defining a time-varying media event to act as a trigger point. | 0.5 |
7,490,167 | 31 | 33 | 31. The system recited in claim 1 , wherein the data service layer comprises schema domain managers for providing standardized logic to different application servers. | 31. The system recited in claim 1 , wherein the data service layer comprises schema domain managers for providing standardized logic to different application servers. 33. The system recited in claim 31 , wherein the standardized logic includes an asset manager EJB for creating, removing and updating content. | 0.702929 |
8,589,359 | 1 | 3 | 1. A method for verifying accuracy of geo-location databases in a cognitive radio communication system comprising of a plurality of protected entities and a plurality of secondary devices, the method comprising: registering the plurality of secondary devices at one or more geo-location databases, wherein the one or more geo-location databases maintain a list of available channels from a plurality of channels, reserved for the plurality of protected entities, for communication by the plurality of secondary devices, and wherein the protected entities are licensed users of the cognitive radio communication system and secondary devices are unlicensed users of the cognitive radio communication system; determining at least one geographical location to access; accessing at least one geo-location database with said geographical location, to obtain database results; comparing the obtained database results to a reference result; and establishing an error condition for any comparisons that exceed an allowed pre-determined difference. | 1. A method for verifying accuracy of geo-location databases in a cognitive radio communication system comprising of a plurality of protected entities and a plurality of secondary devices, the method comprising: registering the plurality of secondary devices at one or more geo-location databases, wherein the one or more geo-location databases maintain a list of available channels from a plurality of channels, reserved for the plurality of protected entities, for communication by the plurality of secondary devices, and wherein the protected entities are licensed users of the cognitive radio communication system and secondary devices are unlicensed users of the cognitive radio communication system; determining at least one geographical location to access; accessing at least one geo-location database with said geographical location, to obtain database results; comparing the obtained database results to a reference result; and establishing an error condition for any comparisons that exceed an allowed pre-determined difference. 3. The method of claim 1 , wherein the reference result is determined by one of the following: a regulatory database, an approved reference calculation, a second geo-location database. | 0.797802 |
8,214,344 | 6 | 7 | 6. A non-transitory computer readable storage medium containing instructions for automatically determining inferences, which when executed by a computer processor, cause the processor to: receive streaming text as input to a computing device; determine at least one inference regarding subject matter of the text based on one or more web searches of one or more terms within the text as the text is being streamed as input, wherein instructions that cause the processor to determine at least one inference comprise instructions that cause the processor to: automatically perform at least one web search of one or more terms within the text; analyze web search results of the at least one web search to determine a number of hits; use the web search results to determine one or more topics when the number of hits is below a predetermined threshold; and determine at least one inference based on the one or more topics; and automatically display the at least one inference upon the at least one inference being determined. | 6. A non-transitory computer readable storage medium containing instructions for automatically determining inferences, which when executed by a computer processor, cause the processor to: receive streaming text as input to a computing device; determine at least one inference regarding subject matter of the text based on one or more web searches of one or more terms within the text as the text is being streamed as input, wherein instructions that cause the processor to determine at least one inference comprise instructions that cause the processor to: automatically perform at least one web search of one or more terms within the text; analyze web search results of the at least one web search to determine a number of hits; use the web search results to determine one or more topics when the number of hits is below a predetermined threshold; and determine at least one inference based on the one or more topics; and automatically display the at least one inference upon the at least one inference being determined. 7. The non-transitory computer readable storage medium of claim 6 further containing additional instructions, which when executed by the processor, cause the processor to: use as search terms in at least one next web search a larger combination of terms within the text than previously used in the at least one web search when the number of hits returned by the at least one web search is above the predetermined threshold. | 0.5 |
8,803,812 | 18 | 19 | 18. The mobile communication device of claim 17 , wherein said instructions, when executed on said processor, further cause said mobile communication device to perform operations comprising: ranking the symbol variants within at least one of the plurality of sets of symbol variants in decreasing order of frequencies of use of the symbol variants. | 18. The mobile communication device of claim 17 , wherein said instructions, when executed on said processor, further cause said mobile communication device to perform operations comprising: ranking the symbol variants within at least one of the plurality of sets of symbol variants in decreasing order of frequencies of use of the symbol variants. 19. The mobile communication device of claim 18 , wherein said instructions, when executed on said processor, further cause said mobile communication device to perform operations comprising: displaying a list of symbol variants, wherein the list of symbol variants comprises the plurality of sets of symbol variants in order of decreasing priority. | 0.5 |
9,858,506 | 11 | 12 | 11. A method comprising: applying, by one or more processors that execute instructions stored in one or more memories for automated optical symbol recognition, blocking to an image stored in at least one of the memories to decompose the image into an ordered set of symbol variants, wherein the image depicts a mathematical expression; selecting, by the processors, a most probable path from among candidate paths corresponding to the ordered set of symbol variants, wherein the candidate paths corresponding to the ordered set of symbol variants each comprise one or more arcs, wherein each of the arcs encompasses an ordered subset of the ordered set of symbol variants, wherein each of the arcs is associated with an arc weight, and wherein the most probable path is selected based on the arc weight of one or more of the arcs; using the most probable path and the ordered set of symbol variants to generate an encoded mathematical expression equivalent to the mathematical expression; and storing the encoded mathematical expression in one or more of the memories. | 11. A method comprising: applying, by one or more processors that execute instructions stored in one or more memories for automated optical symbol recognition, blocking to an image stored in at least one of the memories to decompose the image into an ordered set of symbol variants, wherein the image depicts a mathematical expression; selecting, by the processors, a most probable path from among candidate paths corresponding to the ordered set of symbol variants, wherein the candidate paths corresponding to the ordered set of symbol variants each comprise one or more arcs, wherein each of the arcs encompasses an ordered subset of the ordered set of symbol variants, wherein each of the arcs is associated with an arc weight, and wherein the most probable path is selected based on the arc weight of one or more of the arcs; using the most probable path and the ordered set of symbol variants to generate an encoded mathematical expression equivalent to the mathematical expression; and storing the encoded mathematical expression in one or more of the memories. 12. The method of claim 11 , wherein applying blocking to the image further comprises: setting a blocking-direction indication to indicate one of a horizontal blocking direction or a vertical blocking-direction; setting a current-level indication to indicate a first level; blocking the image into sub-images at a level according to the current-level indication and in a direction according to the blocking-direction indication; and recursively for each sub-image in the sub-images at the current level: applying one or more symbol-recognition methods to the sub-image; and in response to a failure of the application of the symbol-recognition methods to identify the sub-image as a single-symbol-containing sub-image, setting the blocking-direction indication to the other of the horizontal-blocking direction or the vertical-blocking direction, advancing the current-level indication, and recursively applying blocking to the sub-image using the blocking-direction indication and the current-level indication. | 0.5 |
8,903,813 | 14 | 19 | 14. A computer system comprising: a central processing unit (CPU), a computer readable memory, and a non-transitory computer readable storage media; first program instructions to receive, from a requesting computer, a first set of binary data that describes a synthetic event, wherein the synthetic event is a non-executable descriptor of a set of context-related factors; second program instructions to perform a context-based search of a database of electronic files to identify a relevant electronic file, wherein the relevant electronic file comprises the synthetic event; third program instructions to search of the relevant electronic file for at least one non-synthetic event element, wherein the non-synthetic event element is absent from the synthetic event; fourth program instructions to, in response to determining that the relevant electronic file comprises said at least one non-synthetic event, transmit a second set of binary data to the requesting computer, wherein the second set of binary data comprises the relevant electronic file and a description of an identified non-synthetic event element within the relevant electronic file; fifth program instructions to limit the context-based search to search only files that are not related to activities that generated the synthetic event, wherein an activity that generated the synthetic event was medical disease research, and wherein the context-based search is limited to searching non-medical literature; and sixth program instructions to establish a connection between the synthetic event and non-synthetic event elements found in the non-medical literature; and wherein the first, second, third, fourth, fifth, and sixth program instructions are stored on the non-transitory computer readable storage media for execution by the CPU via the computer readable memory. | 14. A computer system comprising: a central processing unit (CPU), a computer readable memory, and a non-transitory computer readable storage media; first program instructions to receive, from a requesting computer, a first set of binary data that describes a synthetic event, wherein the synthetic event is a non-executable descriptor of a set of context-related factors; second program instructions to perform a context-based search of a database of electronic files to identify a relevant electronic file, wherein the relevant electronic file comprises the synthetic event; third program instructions to search of the relevant electronic file for at least one non-synthetic event element, wherein the non-synthetic event element is absent from the synthetic event; fourth program instructions to, in response to determining that the relevant electronic file comprises said at least one non-synthetic event, transmit a second set of binary data to the requesting computer, wherein the second set of binary data comprises the relevant electronic file and a description of an identified non-synthetic event element within the relevant electronic file; fifth program instructions to limit the context-based search to search only files that are not related to activities that generated the synthetic event, wherein an activity that generated the synthetic event was medical disease research, and wherein the context-based search is limited to searching non-medical literature; and sixth program instructions to establish a connection between the synthetic event and non-synthetic event elements found in the non-medical literature; and wherein the first, second, third, fourth, fifth, and sixth program instructions are stored on the non-transitory computer readable storage media for execution by the CPU via the computer readable memory. 19. The computer system of claim 14 , further comprising: seventh program instructions to rank a source of the relevant electronic file, wherein the ranking is based on a public reputation of the source; and eighth program instructions to weight the identified non-synthetic event element based on said ranking; and wherein the seventh and eighth program instructions are stored on the non-transitory computer readable storage media for execution by the CPU via the computer readable memory. | 0.5723 |
8,479,094 | 1 | 9 | 1. A computer implemented method of assisting a user in preparing a written document, comprising: receiving a user selected citation format for a document; determining a user writing style specific to said document via analysis of input entered by said user into said document wherein said user writing style determination is based on formatting applied to said document by said user based on grammatical person of textual input of said input entered in said document by said user; receiving one or more terms entered by said user into said document; submitting search data indicative of said terms to a plurality of reference sources via one or more application programming interface; receiving search results from said reference sources, said search results based on said search data and responsive to said submitting search data indicative of said terms; identifying a subset of said search results; providing aspects of each of said search results identified in said subset to said user for display along with said document; wherein which of said search results are identified in said subset is based on said user writing style specific to said document; and wherein each of said search results provided in said subset is provided in combination with an indication of from which of said reference sources said search result originates; receiving a user selection to add a selected one of said search results of said subset to said document; and inserting, in response to said user selection, text indicative of said selected one of said search results into said document, said text including a citation of said selected one of said search results in said user selected citation format. | 1. A computer implemented method of assisting a user in preparing a written document, comprising: receiving a user selected citation format for a document; determining a user writing style specific to said document via analysis of input entered by said user into said document wherein said user writing style determination is based on formatting applied to said document by said user based on grammatical person of textual input of said input entered in said document by said user; receiving one or more terms entered by said user into said document; submitting search data indicative of said terms to a plurality of reference sources via one or more application programming interface; receiving search results from said reference sources, said search results based on said search data and responsive to said submitting search data indicative of said terms; identifying a subset of said search results; providing aspects of each of said search results identified in said subset to said user for display along with said document; wherein which of said search results are identified in said subset is based on said user writing style specific to said document; and wherein each of said search results provided in said subset is provided in combination with an indication of from which of said reference sources said search result originates; receiving a user selection to add a selected one of said search results of said subset to said document; and inserting, in response to said user selection, text indicative of said selected one of said search results into said document, said text including a citation of said selected one of said search results in said user selected citation format. 9. The method of claim 1 , wherein said text inserted into said document further includes actual search result text from said selected one of said search results. | 0.710714 |
6,073,142 | 1 | 6 | 1. A post office for receiving and redistributing e-mail messages on a computer network, the post office comprising: a receipt mechanism that receives an e-mail message from a sender, the e-mail message having at least one specified recipient; a database of business rules, each business rule specifying an action for controlling the delivery of an e-mail message as a function of an attribute of the e-mail message; a rule engine coupled to receive an e-mail message from the receipt mechanism and coupled to the database to selectively apply the business rules to the e-mail message to determine from selected ones of the business rules a set of actions to be applied to the e-mail message; and a distribution mechanism coupled to receive the set of actions from the rule engine and apply at least one action thereof to the e-mail message to control delivery of the e-mail message and which in response to the rule engine applying an action of deferring delivery of the e-mail message, the distribution engine automatically combines the e-mail message with a new distribution list specifying at least one destination post office for receiving the e-mail message for review by an administrator associated with the destination post office, and a rule history specifying the business rules that were determined to be applicable to the e-mail message by at least one rule engine, and automatically delivers the e-mail message to a first destination post office on the distribution list instead of a specified recipient of the e-mail message. | 1. A post office for receiving and redistributing e-mail messages on a computer network, the post office comprising: a receipt mechanism that receives an e-mail message from a sender, the e-mail message having at least one specified recipient; a database of business rules, each business rule specifying an action for controlling the delivery of an e-mail message as a function of an attribute of the e-mail message; a rule engine coupled to receive an e-mail message from the receipt mechanism and coupled to the database to selectively apply the business rules to the e-mail message to determine from selected ones of the business rules a set of actions to be applied to the e-mail message; and a distribution mechanism coupled to receive the set of actions from the rule engine and apply at least one action thereof to the e-mail message to control delivery of the e-mail message and which in response to the rule engine applying an action of deferring delivery of the e-mail message, the distribution engine automatically combines the e-mail message with a new distribution list specifying at least one destination post office for receiving the e-mail message for review by an administrator associated with the destination post office, and a rule history specifying the business rules that were determined to be applicable to the e-mail message by at least one rule engine, and automatically delivers the e-mail message to a first destination post office on the distribution list instead of a specified recipient of the e-mail message. 6. The post office of claim 1, further comprising: a primary message store, coupled to the receipt engine, for receiving and non-persistently storing e-mail messages; and a secondary message store, accessible to the distribution engine, for receiving therefrom, and persistently storing an e-mail message in response to the rule engine determining that the e-mail message satisfied a business rule requiring the e-mail message to be reviewed by a recipient other than a recipient specified by a sender of the e-mail message. | 0.5 |
7,792,815 | 1 | 2 | 1. A computer implemented user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on analyzing user selections of content items to learn the content preferences of the user according to a context within which the user selected the content and using the learned content preferences to select and order subsequent user content search results when the user is within the same context, the method comprising: providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item, the content items of the set being organized into categories of related content items; receiving incremental input entered by the user for incrementally identifying desired content items of the set, wherein the incremental input includes at least one character in a series of characters; in response to each character of the incremental input entered by the user, presenting a subset of content items of the set to the user; receiving actions from the user selecting content items of the subset; analyzing the descriptive terms of the selected content items to learn content preferences of the user; analyzing microgenre metadata within the selected content items to learn the preferred microgenres of the user, the microgenre metadata characterizing the content items; determining the context in which the user performed the selection actions, the context including at least two of: geographic location of the user; date; day; time; and the category of the selected content items, wherein the context includes at least time; associating the determined contexts of the user selection actions with the user content preferences learned from the corresponding user selections; in response to receiving subsequent incremental input entered by the user, determining a context of said subsequent incremental input and selecting and ordering a collection of content items from the set based on a comparison of the descriptive terms of the content items of the collection with the learned content preferences of the user associated with the determined context in which the user entered the subsequent incremental input, the selecting and ordering further based on a comparison of the microgenre metadata characterizing the content items of the collection with the learned preferred microgenres of the user; and presenting said collection of content items to the user on a display screen. | 1. A computer implemented user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on analyzing user selections of content items to learn the content preferences of the user according to a context within which the user selected the content and using the learned content preferences to select and order subsequent user content search results when the user is within the same context, the method comprising: providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item, the content items of the set being organized into categories of related content items; receiving incremental input entered by the user for incrementally identifying desired content items of the set, wherein the incremental input includes at least one character in a series of characters; in response to each character of the incremental input entered by the user, presenting a subset of content items of the set to the user; receiving actions from the user selecting content items of the subset; analyzing the descriptive terms of the selected content items to learn content preferences of the user; analyzing microgenre metadata within the selected content items to learn the preferred microgenres of the user, the microgenre metadata characterizing the content items; determining the context in which the user performed the selection actions, the context including at least two of: geographic location of the user; date; day; time; and the category of the selected content items, wherein the context includes at least time; associating the determined contexts of the user selection actions with the user content preferences learned from the corresponding user selections; in response to receiving subsequent incremental input entered by the user, determining a context of said subsequent incremental input and selecting and ordering a collection of content items from the set based on a comparison of the descriptive terms of the content items of the collection with the learned content preferences of the user associated with the determined context in which the user entered the subsequent incremental input, the selecting and ordering further based on a comparison of the microgenre metadata characterizing the content items of the collection with the learned preferred microgenres of the user; and presenting said collection of content items to the user on a display screen. 2. The method of claim 1 , further comprising weighting the learned content preferences of the user according to at least one of a measure of recency of selection of the content item having the analyzed descriptive terms, number of selections of the content item having the analyzed descriptive terms, and time of use of the content item having the analyzed descriptive terms, wherein the act of selecting and ordering the collection of content items is further based on the weighted learned content preferences so that content items associated with descriptive terms comparable to the learned content preferences having relatively higher weights are ranked relatively more highly. | 0.619128 |
8,886,518 | 17 | 22 | 17. A translation system comprising: a device; an automatic translator module executable and stored on the device and configured to automatically convert a capitalized source text to lower case text and translate the lower case text to a target text; an aligner configured to determine an alignment between one or more phrases in the capitalized source text and one or more respective phrases in the target text of a capitalization configuration; and a capitalization module configured to recover a capitalized text from the target text according to capitalization information in the capitalized source text and the target text and the alignment determined by the aligner, and to capitalize the target text, the capitalization of the target text including: generating a plurality of capitalization configurations for the target text; for each capitalization configuration, computing a feature probability for each of a plurality of capitalization model feature functions; associating a feature weight with each capitalization model feature function; applying the associated feature weight to the respective computed feature probability for each of the plurality of capitalization model feature functions; for each capitalization configuration, calculating a capitalization configuration probability based on a weighted sum of the computed feature probabilities and applied feature weights, and based on the alignment between the one or more phrases in the capitalized source text and the one or more phrases in the target text or between the capitalized source text and the capitalization configuration; assigning the calculated capitalization configuration probability to each respective capitalization configuration; and selecting the best capitalization configuration from the plurality of capitalization configurations based on the highest calculated capitalization configuration probability. | 17. A translation system comprising: a device; an automatic translator module executable and stored on the device and configured to automatically convert a capitalized source text to lower case text and translate the lower case text to a target text; an aligner configured to determine an alignment between one or more phrases in the capitalized source text and one or more respective phrases in the target text of a capitalization configuration; and a capitalization module configured to recover a capitalized text from the target text according to capitalization information in the capitalized source text and the target text and the alignment determined by the aligner, and to capitalize the target text, the capitalization of the target text including: generating a plurality of capitalization configurations for the target text; for each capitalization configuration, computing a feature probability for each of a plurality of capitalization model feature functions; associating a feature weight with each capitalization model feature function; applying the associated feature weight to the respective computed feature probability for each of the plurality of capitalization model feature functions; for each capitalization configuration, calculating a capitalization configuration probability based on a weighted sum of the computed feature probabilities and applied feature weights, and based on the alignment between the one or more phrases in the capitalized source text and the one or more phrases in the target text or between the capitalized source text and the capitalization configuration; assigning the calculated capitalization configuration probability to each respective capitalization configuration; and selecting the best capitalization configuration from the plurality of capitalization configurations based on the highest calculated capitalization configuration probability. 22. The translation system of claim 17 wherein the capitalization module further includes an initial position model feature function. | 0.521583 |
7,765,200 | 25 | 36 | 25. A computer readable storage medium containing program instructions executable on a processing system for query problem determination; the program instructions comprising: receiving a database query; creating a query execution plan for problem determination of the database query, the query execution plan comprising a plurality of query plan operators; executing the query execution plan; displaying a progress indicator for each of the plurality of query plan operators during execution of the query execution plan in response to progress information; communicating progress information to a query progress analyzer for performing debugging operations; and performing debugging operations during execution of the query execution plan by a query progress analyzer, wherein performing the debugging operations comprises: skipping at least a portion of the query execution plan; pausing execution of the query execution plan at a designated point; inspecting the query execution plan including one or more of looking up variables in the query execution plan and stepping through the query execution plan to inspect different portions of the query execution for problem determination, while the query execution is paused; resuming execution of the query execution plan from the designated point; and providing updated progress information to update display of the progress indicator, wherein upon the query execution plan's invocation, a wrapper function intervenes and jumps into debugging logic which increments a counter for the current query plan operator, and executes any additional instructions corresponding to the query plan operator, wherein the additional instructions include graphically displaying the progress as an enhancement to a query explain tool, wherein the query explain tool would poll the current status of the actual cardinalities processed for each part of the query execution plan and utilizing the existing explain facility. | 25. A computer readable storage medium containing program instructions executable on a processing system for query problem determination; the program instructions comprising: receiving a database query; creating a query execution plan for problem determination of the database query, the query execution plan comprising a plurality of query plan operators; executing the query execution plan; displaying a progress indicator for each of the plurality of query plan operators during execution of the query execution plan in response to progress information; communicating progress information to a query progress analyzer for performing debugging operations; and performing debugging operations during execution of the query execution plan by a query progress analyzer, wherein performing the debugging operations comprises: skipping at least a portion of the query execution plan; pausing execution of the query execution plan at a designated point; inspecting the query execution plan including one or more of looking up variables in the query execution plan and stepping through the query execution plan to inspect different portions of the query execution for problem determination, while the query execution is paused; resuming execution of the query execution plan from the designated point; and providing updated progress information to update display of the progress indicator, wherein upon the query execution plan's invocation, a wrapper function intervenes and jumps into debugging logic which increments a counter for the current query plan operator, and executes any additional instructions corresponding to the query plan operator, wherein the additional instructions include graphically displaying the progress as an enhancement to a query explain tool, wherein the query explain tool would poll the current status of the actual cardinalities processed for each part of the query execution plan and utilizing the existing explain facility. 36. The computer readable medium of claim 25 , wherein displaying a progress indicator for each of the plurality of query plan operators comprises: displaying the plurality of query plan operators in a tree; and displaying the progress indicator for each of the plurality of query plan operators below the respective query plan operator in the tree. | 0.618996 |
8,831,947 | 1 | 2 | 1. A method for extracting a term comprising at least one word from an audio signal captured in a call center environment, comprising: receiving the audio signal captured in an environment; extracting a multiplicity of vectors of spectrum-based features from the audio signal, wherein the spectrum-based features comprise at least any one of Mel Frequency Cepstral Coefficients (MFCC), Delta Cepstral Mel Frequency Coefficients (DMFCC), or spectral energy transform; creating a phoneme lattice from the multiplicity of feature vectors, the phoneme lattice comprising at least one allophone, the at least one allophone comprising at least two phonemes and determined as most probable and correspondingly assigned a probability score; creating a hybrid phoneme-word lattice from the phoneme lattice by utilizing a speech model and a non-speech model that were created from the audio signal captured in the call center environment; and extracting the word by analyzing the hybrid phoneme-word lattice. | 1. A method for extracting a term comprising at least one word from an audio signal captured in a call center environment, comprising: receiving the audio signal captured in an environment; extracting a multiplicity of vectors of spectrum-based features from the audio signal, wherein the spectrum-based features comprise at least any one of Mel Frequency Cepstral Coefficients (MFCC), Delta Cepstral Mel Frequency Coefficients (DMFCC), or spectral energy transform; creating a phoneme lattice from the multiplicity of feature vectors, the phoneme lattice comprising at least one allophone, the at least one allophone comprising at least two phonemes and determined as most probable and correspondingly assigned a probability score; creating a hybrid phoneme-word lattice from the phoneme lattice by utilizing a speech model and a non-speech model that were created from the audio signal captured in the call center environment; and extracting the word by analyzing the hybrid phoneme-word lattice. 2. The method of claim 1 wherein creating a phoneme lattice comprises performing Viterbi decoding on the feature vectors. | 0.853511 |
8,726,395 | 10 | 13 | 10. A method for partial encryption of a document, the method comprising: Paginating, by at least one processor, the document into at least one sub-page according to characteristics of a specific device class; and separately encrypting, by the at least one processor, a to-be-encrypted element of the at least one sub-page using a partial encryption mechanism known by a client device and based on the characteristics of the specific device class, wherein when the to-be-encrypted element of the at least one sub-page comprises the entire sub-page, an encryptor encrypts the entire sub-page, and when the to-be-encrypted element of the at least one sub-page comprises less than all elements of the entire sub-page, the encryptor encrypts only the to-be-encrypted element, and further wherein the to-be-encrypted element of the at least one sub-page is surrounded with a corresponding encryption tag. | 10. A method for partial encryption of a document, the method comprising: Paginating, by at least one processor, the document into at least one sub-page according to characteristics of a specific device class; and separately encrypting, by the at least one processor, a to-be-encrypted element of the at least one sub-page using a partial encryption mechanism known by a client device and based on the characteristics of the specific device class, wherein when the to-be-encrypted element of the at least one sub-page comprises the entire sub-page, an encryptor encrypts the entire sub-page, and when the to-be-encrypted element of the at least one sub-page comprises less than all elements of the entire sub-page, the encryptor encrypts only the to-be-encrypted element, and further wherein the to-be-encrypted element of the at least one sub-page is surrounded with a corresponding encryption tag. 13. The method of claim 10 , further comprising: identifying the to-be-encrypted element of the at least one sub-page by a corresponding encryption tag surrounding the to-be-encrypted element. | 0.767554 |
6,012,052 | 61 | 62 | 61. In a system including a server for servicing requests, from a number of clients, for resources, device for generating resource transition probabilities, the device comprising: a) a log generation unit for generating usage logs based on said requests; and b) a resource transition probability generation unit for determining the resource transition probabilities based on the usage logs. | 61. In a system including a server for servicing requests, from a number of clients, for resources, device for generating resource transition probabilities, the device comprising: a) a log generation unit for generating usage logs based on said requests; and b) a resource transition probability generation unit for determining the resource transition probabilities based on the usage logs. 62. The device of claim 61 wherein the usage logs include information regarding (i) an identification of the clients which requested the resources, (ii) an identification of the resources referenced, and (iii) an identification of times when the resources were referenced. | 0.764298 |
9,047,285 | 12 | 17 | 12. The method of claim 1 , wherein the first frame is a benefit frame. | 12. The method of claim 1 , wherein the first frame is a benefit frame. 17. The method of claim 12 , wherein the benefit frame comprises a benefactor role. | 0.629464 |
8,001,064 | 30 | 32 | 30. A computer program product having a non-transitory computer-readable medium having computer program instructions recorded thereon for learning user preferences in a search of knowledge base to construct one or more profiles for producing personalized search results, the computer program instruction comprising instructions for: receiving feedback from the user regarding quality of search results presented to the user in a search of a knowledge base that is a semantic network of relationships among concepts and that provides an index of a plurality of documents, the feedback representing how well the search results match an input query provided by the user, the search results including one or more of the documents indexed by the knowledge base; constructing the one or more profiles for the user based on the feedback received, where for each of the search results that receive feedback, a plurality of feedback values are determined and are used to construct a model that includes profile weights computed from the feedback values; modifying internal weights used for scoring search criteria applied in producing the search results presented to the user, the modifications made by combining the internal weights with the profile weights in the constructed model, wherein the internal weights are modified according to a function of the internal weights used for scoring search criteria and of the profile weights; generating implicit search criteria for the user based on the one or more profiles; and applying the implicit search criteria and modified weights during a subsequent search of the knowledge base conducted by the user producing a subsequent set of search results that are personalized to the user. | 30. A computer program product having a non-transitory computer-readable medium having computer program instructions recorded thereon for learning user preferences in a search of knowledge base to construct one or more profiles for producing personalized search results, the computer program instruction comprising instructions for: receiving feedback from the user regarding quality of search results presented to the user in a search of a knowledge base that is a semantic network of relationships among concepts and that provides an index of a plurality of documents, the feedback representing how well the search results match an input query provided by the user, the search results including one or more of the documents indexed by the knowledge base; constructing the one or more profiles for the user based on the feedback received, where for each of the search results that receive feedback, a plurality of feedback values are determined and are used to construct a model that includes profile weights computed from the feedback values; modifying internal weights used for scoring search criteria applied in producing the search results presented to the user, the modifications made by combining the internal weights with the profile weights in the constructed model, wherein the internal weights are modified according to a function of the internal weights used for scoring search criteria and of the profile weights; generating implicit search criteria for the user based on the one or more profiles; and applying the implicit search criteria and modified weights during a subsequent search of the knowledge base conducted by the user producing a subsequent set of search results that are personalized to the user. 32. The computer program product of claim 30 , wherein at least one of the one or more profiles is a user profile that pertains to the user's general preferences that are not specifically associated with a search, and wherein at least one of the one or more profiles is a search profile that pertains to a particular query or query type not specifically associated with the user. | 0.5 |
8,160,877 | 1 | 2 | 1. A method for real-time speaker recognition, comprising: obtaining speech data of a speaker to identify the speaker from a plurality of speakers; extracting, using a processor of a computer, a coarse feature of the speaker from the speech data; identifying the speaker as belonging to a pre-determined speaker cluster that is one of a plurality of partitions of the plurality of speakers and corresponds to a subset of a plurality of biometric signatures of the plurality of speakers, wherein identifying the speaker as belonging to the pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a speaker independent parameter representing the subset of the plurality of biometric signatures; further identifying, in response to identifying the speaker as belonging to the pre-determined speaker cluster, the speaker as belonging to a second level pre-determined speaker cluster that is one of a plurality of second level partitions of the pre-determined speaker cluster and corresponds to a second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the second level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a second level speaker independent parameter representing the second level subset of the subset of the plurality of biometric signatures; extracting, using the processor of the computer, a plurality of Mel-Frequency Cepstral Coefficients (MFCC) and a plurality of Gaussian Mixture Model (GMM) components from the speech data; determining a biometric signature of the speaker based on the plurality of MFCC and the plurality of GMM components; and determining in real time, using the processor of the computer, an identity of the speaker by comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of biometric signatures is specific to one of the plurality of speakers. | 1. A method for real-time speaker recognition, comprising: obtaining speech data of a speaker to identify the speaker from a plurality of speakers; extracting, using a processor of a computer, a coarse feature of the speaker from the speech data; identifying the speaker as belonging to a pre-determined speaker cluster that is one of a plurality of partitions of the plurality of speakers and corresponds to a subset of a plurality of biometric signatures of the plurality of speakers, wherein identifying the speaker as belonging to the pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a speaker independent parameter representing the subset of the plurality of biometric signatures; further identifying, in response to identifying the speaker as belonging to the pre-determined speaker cluster, the speaker as belonging to a second level pre-determined speaker cluster that is one of a plurality of second level partitions of the pre-determined speaker cluster and corresponds to a second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the second level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a second level speaker independent parameter representing the second level subset of the subset of the plurality of biometric signatures; extracting, using the processor of the computer, a plurality of Mel-Frequency Cepstral Coefficients (MFCC) and a plurality of Gaussian Mixture Model (GMM) components from the speech data; determining a biometric signature of the speaker based on the plurality of MFCC and the plurality of GMM components; and determining in real time, using the processor of the computer, an identity of the speaker by comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of biometric signatures is specific to one of the plurality of speakers. 2. The method of claim 1 , wherein each of the plurality of partitions of the plurality of speakers comprises one of a plurality of speaker gender clusters, wherein each of the plurality of second level partitions of the pre-determined speaker cluster comprises one of a plurality of speaker age clusters, the method further comprising: further identifying, in response to identifying the speaker as belonging to the second level pre-determined speaker cluster, the speaker as belonging to a third level pre-determined speaker cluster that is one of a plurality of third level partitions of the second level pre-determined speaker cluster and corresponds to a third level subset of the second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the third level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a third level speaker independent parameter representing the third level subset of the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of third level partitions of the second level pre-determined speaker cluster comprises one of a plurality of speaker native language clusters, and wherein comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures is limited to comparing to the third level subset of the second level subset of the subset of the plurality of biometric signatures. | 0.5 |
8,655,659 | 18 | 21 | 18. A communication terminal capable of text transmission and speech session, wherein a number of the communication terminals are connected to each other through a wireless communication network or a wired communication network, so that a text transmission or speech session can be carried out therebetween, wherein the communication terminal comprises a text transmission synthesizing device, a speech session device and the personalized text-to-speech synthesizing device according to claim 1 . | 18. A communication terminal capable of text transmission and speech session, wherein a number of the communication terminals are connected to each other through a wireless communication network or a wired communication network, so that a text transmission or speech session can be carried out therebetween, wherein the communication terminal comprises a text transmission synthesizing device, a speech session device and the personalized text-to-speech synthesizing device according to claim 1 . 21. The communication terminal according to claim 18 , wherein the communication terminal is a computer client. | 0.952846 |
9,260,122 | 3 | 4 | 3. The method of claim 1 , further comprising: determining the cross-frame constraint (Φ(s k t , s l t+1 ) according to: Φ ( s k t , s l t + 1 ) = max ( ( F ( s k t - s l t + 1 ; λ ) , ( F ( s k t - s l t + 1 + ∈ ; λ ) ) ; wherein λ=[μ f , σ f , μ v , Σ v , τ], (μ f , σ f ) and models a Gaussian distribution of an object state at a next frame given its state at the previous frame; “τ” is the determined speed of the movement of the cameras relative to the objects; and F( ) is a distance function that computes a matching score for each pair of object states (s k t , s l t+1 ), given an object state (s k t ) at frame (t), and (s l t+1 ) at frame (t+1), wherein (k) and (l) may be different views, and wherein (s k t ) and (s l t+1 ) may correspond to a same object or to two different, adjacent objects. | 3. The method of claim 1 , further comprising: determining the cross-frame constraint (Φ(s k t , s l t+1 ) according to: Φ ( s k t , s l t + 1 ) = max ( ( F ( s k t - s l t + 1 ; λ ) , ( F ( s k t - s l t + 1 + ∈ ; λ ) ) ; wherein λ=[μ f , σ f , μ v , Σ v , τ], (μ f , σ f ) and models a Gaussian distribution of an object state at a next frame given its state at the previous frame; “τ” is the determined speed of the movement of the cameras relative to the objects; and F( ) is a distance function that computes a matching score for each pair of object states (s k t , s l t+1 ), given an object state (s k t ) at frame (t), and (s l t+1 ) at frame (t+1), wherein (k) and (l) may be different views, and wherein (s k t ) and (s l t+1 ) may correspond to a same object or to two different, adjacent objects. 4. The method of claim 3 , further comprising defining the optimal state path for the detection of the object by: determining confidence scores for the object detection states according to real-time dynamic programming formulations: χ k 1 = ψ ( s k 1 ) ; and χ k t = ψ ( s k t ) max j ( χ k t - 1 ϕ ( s k t , s j t - 1 ) ) ; at each time point, selecting an optimal object state (s v t ) according to formulation: v = arg max k ( χ k t ) ; inferring suboptimal object states in other camera views at each time point (t); and if no object detection is found at a time point (t), restarting the steps of determining confidence scores for the object detection states via the real-time dynamic programming formulations and selecting an optimal object state (s v t ) at a next time point (t+1). | 0.5 |
8,600,736 | 1 | 14 | 1. A method of operating a computer to perform linguistic analysis, comprising the steps of: splitting an input text into words and sentences; for each sentence, comparing phrases in each sentence with known phrases stored in a database, as follows: for each word in the sentence, comparing a value thereof and values of the words following it with values of words of stored phrases; in the event a match is found between the value of at least two consecutive words and the value of words of a stored phrase, labelling the matched at least two consecutive words with an overphrase that describes the matched value; after a penultimate word has been compared, recasting the sentence by replacing the matched phrases by respective overphrases; and then repeating the step of comparing phrases with the recast sentence until there is no further recasting by the step of using the overphrase in the comparison as a word until there is no further match found. | 1. A method of operating a computer to perform linguistic analysis, comprising the steps of: splitting an input text into words and sentences; for each sentence, comparing phrases in each sentence with known phrases stored in a database, as follows: for each word in the sentence, comparing a value thereof and values of the words following it with values of words of stored phrases; in the event a match is found between the value of at least two consecutive words and the value of words of a stored phrase, labelling the matched at least two consecutive words with an overphrase that describes the matched value; after a penultimate word has been compared, recasting the sentence by replacing the matched phrases by respective overphrases; and then repeating the step of comparing phrases with the recast sentence until there is no further recasting by the step of using the overphrase in the comparison as a word until there is no further match found. 14. A method according to claim 1 , wherein word sense disambiguation is addressed by loading all word senses for each word and deselecting those senses that are not valid in the text provided. | 0.692675 |
9,449,287 | 1 | 4 | 1. A method for predicting a personality of at least one human subject, the method comprising: receiving data associated with the at least one human subject from one or more sources; clustering the data based on one or more topics of interest of the at least one human subject using one or more topic modeling algorithms; predicting at least one high level personality trait associated with the at least one human subject by analyzing the clustered data, the at least one high level personality trait being one of one or more high level personality traits defined by a first model; and predicting at least one personality profile by classifying the at least one high level personality trait into one or more granular level personality traits defined by a second model, the classifying being based on clustered data, wherein at least one of the receiving data, the clustering the data, the predicting at least one first personality, and the predicting at least one second personality is performed by a processor. | 1. A method for predicting a personality of at least one human subject, the method comprising: receiving data associated with the at least one human subject from one or more sources; clustering the data based on one or more topics of interest of the at least one human subject using one or more topic modeling algorithms; predicting at least one high level personality trait associated with the at least one human subject by analyzing the clustered data, the at least one high level personality trait being one of one or more high level personality traits defined by a first model; and predicting at least one personality profile by classifying the at least one high level personality trait into one or more granular level personality traits defined by a second model, the classifying being based on clustered data, wherein at least one of the receiving data, the clustering the data, the predicting at least one first personality, and the predicting at least one second personality is performed by a processor. 4. The method of claim 1 , further comprising performing a people search based on the at least one high level personality trait, the at least one personality profile, demographic distribution, gender, and the at least one topic of interest. | 0.72093 |
8,676,815 | 1 | 6 | 1. A system, comprising: at least one memory having stored therein computer executable instructions; and a processor, coupled to the at least one memory, configured to execute or facilitate execution of the computer executable instructions to at least: create a suffix tree document model that is a representation of a plurality of documents; convert the suffix tree document model into a vector document model that is a representation of a document of the plurality of documents to form the suffix tree document model converted into the vector document model, wherein the vector document model is a vector with M elements and M is a total number of nodes in the suffix tree document model; weight elements of the suffix tree document model converted into the vector document model; and determine a similarity between two or more weighted vector document models, each representing a respective document of the plurality of documents. | 1. A system, comprising: at least one memory having stored therein computer executable instructions; and a processor, coupled to the at least one memory, configured to execute or facilitate execution of the computer executable instructions to at least: create a suffix tree document model that is a representation of a plurality of documents; convert the suffix tree document model into a vector document model that is a representation of a document of the plurality of documents to form the suffix tree document model converted into the vector document model, wherein the vector document model is a vector with M elements and M is a total number of nodes in the suffix tree document model; weight elements of the suffix tree document model converted into the vector document model; and determine a similarity between two or more weighted vector document models, each representing a respective document of the plurality of documents. 6. The system of claim 1 , wherein the processor is further configured to execute or facilitate the execution of the computer executable instructions to populate the suffix tree document model or clean data for conversion. | 0.778884 |
8,970,404 | 11 | 12 | 11. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor of a communication device, cause the communication device to perform a text coding and decoding method, the text coding and decoding method comprising: creating a coding table for indexing each character coded in a five-bit first string; receiving text data, and convert each upper case letter of the text data into a lower case letter; determining the five-bit first string of each character of the text data according the coding table; lining up the determined five-bit first strings in sequence to form an array; dividing the array into eight-bit second strings; supplementing a last string with preset bits to form an eight-bit string when the last string is less than eight bits; embedding a preset indicatory code in a head of the array; embedding a data coding scheme and a length of the array in a header of the array to code the text data; transmitting the array to a receiver that communicates with the communication device; receiving the array from a sending device that communicates with the communication device, wherein the communication device saves the coding table for indexing each character coded in the five-bit first string; determining whether the array is coded in the data coding scheme according to the preset indicatory code of the array, wherein the data coding scheme codes the array by the coding table saved in the communication device; converting the array into the five-bit first strings according to the data coding scheme embedded in the header of the array when the array is coded by the data coding scheme; indexing the coding table for determining the character of each string; and displaying a text data corresponding to the determined character of each string on a monitor of the communication device. | 11. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor of a communication device, cause the communication device to perform a text coding and decoding method, the text coding and decoding method comprising: creating a coding table for indexing each character coded in a five-bit first string; receiving text data, and convert each upper case letter of the text data into a lower case letter; determining the five-bit first string of each character of the text data according the coding table; lining up the determined five-bit first strings in sequence to form an array; dividing the array into eight-bit second strings; supplementing a last string with preset bits to form an eight-bit string when the last string is less than eight bits; embedding a preset indicatory code in a head of the array; embedding a data coding scheme and a length of the array in a header of the array to code the text data; transmitting the array to a receiver that communicates with the communication device; receiving the array from a sending device that communicates with the communication device, wherein the communication device saves the coding table for indexing each character coded in the five-bit first string; determining whether the array is coded in the data coding scheme according to the preset indicatory code of the array, wherein the data coding scheme codes the array by the coding table saved in the communication device; converting the array into the five-bit first strings according to the data coding scheme embedded in the header of the array when the array is coded by the data coding scheme; indexing the coding table for determining the character of each string; and displaying a text data corresponding to the determined character of each string on a monitor of the communication device. 12. The storage medium of claim 11 , wherein the text coding and decoding method further comprises: embedding a page number coded in a five-bit page string in a head of the five-bit first string when the character is not indexed in a first page of the coding table, wherein the coding table comprises a plurality of pages. | 0.5 |
8,965,971 | 29 | 30 | 29. The computer-readable storage medium of claim 27 , wherein the input source corresponds to at least one image data file and the processing comprises: submitting the image data file to an image comparison service; and receiving from the image comparison service, image information based on at least one similar image, wherein the similar data file comprises the at least one similar image. | 29. The computer-readable storage medium of claim 27 , wherein the input source corresponds to at least one image data file and the processing comprises: submitting the image data file to an image comparison service; and receiving from the image comparison service, image information based on at least one similar image, wherein the similar data file comprises the at least one similar image. 30. The computer-readable storage medium of claim 29 , wherein: the similar data file source comprises a website where the at least one similar image was found; and determining the additional information about the at least one similar image comprises extracting information from the website where the at least one similar image was found. | 0.5 |
10,015,177 | 1 | 22 | 1. A method comprising: receiving, by a computer system, first event data indicative of computer network activity of a plurality of users and network devices in a computer network; generating, by the computer system, classification metadata for each of the network devices and users, based on the first event data, to indicate relevance in a network security context of each of the users and network devices; identifying, by the computer system, usage relationships between one or more of the users and one or more of the network devices, based on first event data; assigning, by the computer system, usage similarity scores to the network devices based on the identified usage relationships, the usage similarity scores being indicative of which of the network devices have been used by the same or similar group of users; receiving, by the computer system, second event data indicative of computer network activity of a particular user of the plurality of users; and detecting, by the computer system and in response to the second event data, an anomaly indicative that the particular user has interacted with a particular network device with which the particular user does not normally interact, based on the usage similarity scores and the classification metadata. | 1. A method comprising: receiving, by a computer system, first event data indicative of computer network activity of a plurality of users and network devices in a computer network; generating, by the computer system, classification metadata for each of the network devices and users, based on the first event data, to indicate relevance in a network security context of each of the users and network devices; identifying, by the computer system, usage relationships between one or more of the users and one or more of the network devices, based on first event data; assigning, by the computer system, usage similarity scores to the network devices based on the identified usage relationships, the usage similarity scores being indicative of which of the network devices have been used by the same or similar group of users; receiving, by the computer system, second event data indicative of computer network activity of a particular user of the plurality of users; and detecting, by the computer system and in response to the second event data, an anomaly indicative that the particular user has interacted with a particular network device with which the particular user does not normally interact, based on the usage similarity scores and the classification metadata. 22. The method of claim 1 , further comprising: identifying a security threat based on the detected anomaly by identifying a relationship path from the particular user through one or more anomalies to a network device designated as a critical resource of the network, the critical resource being a server responsible for handling security authentication requests for the network. | 0.8105 |
8,856,946 | 9 | 10 | 9. A computer program product for defining multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by one or more hardware processors to perform a method comprising: receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters; associating one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects; associating the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring; parsing the security-enabled synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object; calculating a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of:
(P(C)+P(S))×Wt(S), where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object; creating multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells; transmitting multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane; defining multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trapping said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap. | 9. A computer program product for defining multiple security-enabled context-based data gravity wells on a security-enabled context-based data gravity wells membrane, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by one or more hardware processors to perform a method comprising: receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters; associating one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects; associating the synthetic context-based object with a security object to generate a security-enabled synthetic context-based object, wherein the security object describes a circumstance that describes an environment in which an event is occurring; parsing the security-enabled synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, a weighting factor of importance of the security-enabled synthetic context-based object, and a probability that the security object has been associated with a correct synthetic context-based object; calculating a virtual mass of the parsed security-enabled synthetic context-based object, wherein the virtual mass of the parsed security-enabled synthetic context-based object is derived from a formula of:
(P(C)+P(S))×Wt(S), where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the security object has been associated with the correct synthetic context-based object, and where Wt(S) is the weighting factor of importance of the security-enabled synthetic context-based object; creating multiple security-enabled context-based data gravity well frameworks on a security-enabled context-based data gravity wells membrane, wherein each of the multiple security-enabled context-based data gravity well frameworks comprises at least one non-contextual data object, at least one context object, and at least one security object, and wherein the security-enabled context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple security-enabled context-based data gravity wells; transmitting multiple parsed security-enabled synthetic context-based objects to the security-enabled context-based data gravity wells membrane; defining multiple security-enabled context-based data gravity wells according to the virtual mass of multiple parsed security-enabled synthetic context-based objects that are pulled into each of the security-enabled context-based data gravity well frameworks, wherein each of the multiple parsed security-enabled synthetic context-based objects is pulled into a particular security-enabled context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object, said at least one context object, and said at least one security object in said particular security-enabled context-based data gravity well; and in response to an unmatched parsed security-enabled synthetic context-based object failing to be pulled into any of the security-enabled context-based data gravity wells, trapping said unmatched parsed security-enabled synthetic context-based object in an unmatched parsed security-enabled synthetic context-based object trap. 10. The computer program product of claim 9 , further comprising program code that is readable and executable by said one or more hardware processors to: process the unmatched parsed security-enabled synthetic context-based object to generate an alert, wherein the alert indicates that the unmatched parsed security-enabled synthetic context-based object represents a financially fraudulent event. | 0.871188 |
10,108,622 | 8 | 13 | 8. A computer-implemented method for managing a database, comprising: maintaining data in a computerized database, said computerized database having data organized in at least one database table; monitoring respective changes made to each database table of said at least one database table; responsive to said monitoring respective changes made to each database table of said at least one database table, determining a respective database table volatility state of each database table of said at least one database table; responsive to determining a respective database table volatility state of each database table of said at least one database table, generating a respective at least one volatility attribute representing the respective database table volatility state of each database table of said at least one database table, wherein the respective database table volatility state of each said database table is a property of the respective database table that is a function of changes to the respective database table with respect to time, independent of any queries against data in the respective database table; and using the respective at least one volatility attribute of each database table to manage data in the respective database table, wherein using the respective at least one volatility attribute of each database table to manage data in the respective database table comprises at least one of: (a) using the respective at least one volatility attribute to determine an optimum query execution strateay for a query against data in the respective database table, (b) using the respective at least one volatility attribute to determine whether to re-optimize a previously saved query execution strateay for a query against data in the respective database table, (c) using the respective at least one volatility attribute to determine whether to collect statistical data regarding the respective database table, and (d) using the respective at least one volatility attribute to manage storage and/or retrieval of data in the respective at least one database table. | 8. A computer-implemented method for managing a database, comprising: maintaining data in a computerized database, said computerized database having data organized in at least one database table; monitoring respective changes made to each database table of said at least one database table; responsive to said monitoring respective changes made to each database table of said at least one database table, determining a respective database table volatility state of each database table of said at least one database table; responsive to determining a respective database table volatility state of each database table of said at least one database table, generating a respective at least one volatility attribute representing the respective database table volatility state of each database table of said at least one database table, wherein the respective database table volatility state of each said database table is a property of the respective database table that is a function of changes to the respective database table with respect to time, independent of any queries against data in the respective database table; and using the respective at least one volatility attribute of each database table to manage data in the respective database table, wherein using the respective at least one volatility attribute of each database table to manage data in the respective database table comprises at least one of: (a) using the respective at least one volatility attribute to determine an optimum query execution strateay for a query against data in the respective database table, (b) using the respective at least one volatility attribute to determine whether to re-optimize a previously saved query execution strateay for a query against data in the respective database table, (c) using the respective at least one volatility attribute to determine whether to collect statistical data regarding the respective database table, and (d) using the respective at least one volatility attribute to manage storage and/or retrieval of data in the respective at least one database table. 13. The computer-implemented method for managing a database of claim 8 , further comprising: receiving a user input specifying whether a database table volatility state of a database table of said at least one database table is to be manually specified or automatically determined by a computer system; wherein said determining a respective database table volatility state of each database table and generating a respective at least one volatility attribute representing the respective database table volatility state of each database table are not performed with respect to a database table for which said user input specifies that the volatility state of the corresponding database table is to be manually specified. | 0.5 |
9,087,236 | 9 | 14 | 9. A system comprising: a computer processor; computer readable storage medium coupled to the computer processor, the computer readable storage medium including one or more documents containing a first flow diagram in one or more diagram formats supported by the documents; an extractor configured to extract from the first flow diagram one or more flow graphs comprising extracted nodes and edges, and extract from the first flow diagram relational, geometric and textual features for the extracted nodes and edges; a classifier trained to learn rules to recognize process semantics based on the relational, geometric and textual features of the extracted nodes and edges, the rules configured as a decision tree; and generated, based on the learned rules, process modeling recognition code to recognize and decide process semantics in a second flow diagram. | 9. A system comprising: a computer processor; computer readable storage medium coupled to the computer processor, the computer readable storage medium including one or more documents containing a first flow diagram in one or more diagram formats supported by the documents; an extractor configured to extract from the first flow diagram one or more flow graphs comprising extracted nodes and edges, and extract from the first flow diagram relational, geometric and textual features for the extracted nodes and edges; a classifier trained to learn rules to recognize process semantics based on the relational, geometric and textual features of the extracted nodes and edges, the rules configured as a decision tree; and generated, based on the learned rules, process modeling recognition code to recognize and decide process semantics in a second flow diagram. 14. The system of claim 9 , wherein the classifier is trained to identify patterns in features of the extracted nodes and edges that indicate a class of process semantic of the respective extracted nodes and edges. | 0.637288 |
9,443,139 | 17 | 18 | 17. The apparatus of claim 16 , wherein the probability associated with a link is one of at least 3 distinct values. | 17. The apparatus of claim 16 , wherein the probability associated with a link is one of at least 3 distinct values. 18. The apparatus of claim 17 , wherein said three distinct values include a positive value, a zero value and a negative value. | 0.5 |
10,074,012 | 1 | 4 | 1. A method, comprising: collecting image data relating to one or more real-world objects or persons from a scene while collecting audio data relating to the one or more real-world objects or persons from the same scene, the audio data being used to derive one or more sound objects corresponding to the one or more real-world objects or persons, the image data being used to derive one or more video objects corresponding to the one or more real-world objects or persons; generating, based on the one or more sound objects and the one or more video objects, one or more candidate salient objects; selecting a salient object from among the one or more candidate salient objects and performing one or more perceptual enhancement operations on the salient object; wherein the method is performed by one or more computing devices. | 1. A method, comprising: collecting image data relating to one or more real-world objects or persons from a scene while collecting audio data relating to the one or more real-world objects or persons from the same scene, the audio data being used to derive one or more sound objects corresponding to the one or more real-world objects or persons, the image data being used to derive one or more video objects corresponding to the one or more real-world objects or persons; generating, based on the one or more sound objects and the one or more video objects, one or more candidate salient objects; selecting a salient object from among the one or more candidate salient objects and performing one or more perceptual enhancement operations on the salient object; wherein the method is performed by one or more computing devices. 4. The method of claim 1 , wherein the one or more perceptual enhancement operations comprise one or more of: increasing loudness of the salient object, moving the salient object closer to a listener, moving non-salient sound not represented by the salient object to different audio objects, or isolating and assigning salient sound represented by the salient object to a separate audio object from other sound. | 0.758803 |
10,116,600 | 18 | 19 | 18. A server that sends a message expressing a statement on behalf of a user, the server comprising: a processor; and a memory storing: an expression set comprising at least one expression authored by the user; and instructions that, when executed by the processor, provide: an expression style identifier that examines the expression set to identify a phrase that appears in at least one prior expression previously authored by the user and that is relevant to the statement; and a message composer that: composes a message expressing the statement in an expression style of the user by: selecting a message template associated with the expression style of the user, wherein the message template comprises at least one slot for insertion of content to adapt the message template to the statement; and inserting the phrase previously authored by the user into the at least one message slot of the message template; presents the message to the user for confirmation; and responsive to receiving a confirmation of the message from the user, transmits the message to the recipient. | 18. A server that sends a message expressing a statement on behalf of a user, the server comprising: a processor; and a memory storing: an expression set comprising at least one expression authored by the user; and instructions that, when executed by the processor, provide: an expression style identifier that examines the expression set to identify a phrase that appears in at least one prior expression previously authored by the user and that is relevant to the statement; and a message composer that: composes a message expressing the statement in an expression style of the user by: selecting a message template associated with the expression style of the user, wherein the message template comprises at least one slot for insertion of content to adapt the message template to the statement; and inserting the phrase previously authored by the user into the at least one message slot of the message template; presents the message to the user for confirmation; and responsive to receiving a confirmation of the message from the user, transmits the message to the recipient. 19. The server of claim 18 , wherein: the statement is associated with a content item; and the message composer composes the message expressing the statement by: identifying a selected content item that is associated with the statement; and composing the message including the selected content item in the message. | 0.5 |
8,265,923 | 1 | 3 | 1. A method comprising: translating source language content in a source natural language to a target natural language using statistical machine translation (SMT) employing a conditional translation probability conditioned on the source language content; and optimizing values of parameters of the conditional translation probability by an iterative optimization process operating on a translation pool, the optimizing including adding candidate aligned translations to the translation pool by sampling available candidate aligned translations for a source language sentence in accordance with the conditional translation probability; wherein the conditional translation probability is quantitatively equivalent to: P ( e , a | f ) = exp ( ∑ k = 1 K λ k h k ( e , a , f ) ) ∑ ( e ′ , a ′ ) ∈ L exp ( ∑ k = 1 K λ k h k ( e ′ , a ′ , f ) ) where f denotes the source language sentence, L denotes the set of available candidate aligned translations, (e, a) denotes a candidate aligned translation for which the conditional probability is computed, h k (. . .), k=1, . . . , K denotes a set of K feature functions, and λ k , k=1, . . . , K denotes the parameters of the conditional translation probability; wherein the sampling comprises: selecting a sampled candidate aligned translation by traversing a translation lattice representing the available candidate aligned translations for the source language sentence from its root node to its final node wherein each transition from a current node to a next node is selected based on conditional translation probabilities σ ( e i ) = ∑ k = 1 K λ k h k ( e i ) of the available edges e i leading away from the current node; wherein the SMT and the optimizing are implemented by a SMT system embodied by at least one digital processor. | 1. A method comprising: translating source language content in a source natural language to a target natural language using statistical machine translation (SMT) employing a conditional translation probability conditioned on the source language content; and optimizing values of parameters of the conditional translation probability by an iterative optimization process operating on a translation pool, the optimizing including adding candidate aligned translations to the translation pool by sampling available candidate aligned translations for a source language sentence in accordance with the conditional translation probability; wherein the conditional translation probability is quantitatively equivalent to: P ( e , a | f ) = exp ( ∑ k = 1 K λ k h k ( e , a , f ) ) ∑ ( e ′ , a ′ ) ∈ L exp ( ∑ k = 1 K λ k h k ( e ′ , a ′ , f ) ) where f denotes the source language sentence, L denotes the set of available candidate aligned translations, (e, a) denotes a candidate aligned translation for which the conditional probability is computed, h k (. . .), k=1, . . . , K denotes a set of K feature functions, and λ k , k=1, . . . , K denotes the parameters of the conditional translation probability; wherein the sampling comprises: selecting a sampled candidate aligned translation by traversing a translation lattice representing the available candidate aligned translations for the source language sentence from its root node to its final node wherein each transition from a current node to a next node is selected based on conditional translation probabilities σ ( e i ) = ∑ k = 1 K λ k h k ( e i ) of the available edges e i leading away from the current node; wherein the SMT and the optimizing are implemented by a SMT system embodied by at least one digital processor. 3. The method as set forth in claim 1 , wherein for a current iteration of the iterative optimization process the sampling is in accordance with the conditional translation probability employing parameter values of the current iteration. | 0.639818 |
8,316,394 | 32 | 34 | 32. The system of claim 20 , wherein the user equipment is configured to associate the user selected first cell of the mosaic page with interactive features. | 32. The system of claim 20 , wherein the user equipment is configured to associate the user selected first cell of the mosaic page with interactive features. 34. The system of claim 32 , wherein the interactive features are selected from the group consisting of a shopping opportunity, a program promotion, a selection of additional program content, a recording button, and availability of interactive program snipes. | 0.5 |
8,666,962 | 1 | 9 | 1. A computer-implemented method of providing speculative search results, comprising: receiving, at a search engine, over a network from a client node, a not-yet-submitted search query provided by a user; in response to receiving at the search engine, from the client node the not-yet-submitted search query, determining, at the search engine, whether the not-yet-submitted search query meets a criterion for initiating a speculative search for items that satisfy the not-yet-submitted search query; in response to determining, at the search engine, that the not-yet-submitted search query does not meet the criterion, waiting for additional input from the client node without initiating the speculative search for items that satisfy the not-yet-submitted search query; receiving, at the search engine, over the network, from the client node, an updated not-yet-submitted search query that comprises the not-yet-submitted search query and one or more additional characters; in response to receiving, at the search engine, from the client node, the updated not-yet-submitted search query, determining, at the search engine, whether the updated not-yet-submitted search query meets the criterion for initiating a speculative search for items that satisfy the updated not-yet-submitted search query; in response to determining, at the search engine, that the updated not-yet-submitted search query meets the criterion, performing, at the search engine, the speculative search for items that satisfy said updated not-yet-submitted search query prior to receiving, from said client node, an indication that said updated not-yet-submitted search query is completely formed; providing, from the search engine, to said client node, information about at least one item, found by the speculative search, that satisfies said updated not-yet submitted search query; wherein the at least one item, found by the speculative search, includes at least one of (a) a web page, (b) a graphic, or (c) textual information; wherein the method is performed by one or more computing devices. | 1. A computer-implemented method of providing speculative search results, comprising: receiving, at a search engine, over a network from a client node, a not-yet-submitted search query provided by a user; in response to receiving at the search engine, from the client node the not-yet-submitted search query, determining, at the search engine, whether the not-yet-submitted search query meets a criterion for initiating a speculative search for items that satisfy the not-yet-submitted search query; in response to determining, at the search engine, that the not-yet-submitted search query does not meet the criterion, waiting for additional input from the client node without initiating the speculative search for items that satisfy the not-yet-submitted search query; receiving, at the search engine, over the network, from the client node, an updated not-yet-submitted search query that comprises the not-yet-submitted search query and one or more additional characters; in response to receiving, at the search engine, from the client node, the updated not-yet-submitted search query, determining, at the search engine, whether the updated not-yet-submitted search query meets the criterion for initiating a speculative search for items that satisfy the updated not-yet-submitted search query; in response to determining, at the search engine, that the updated not-yet-submitted search query meets the criterion, performing, at the search engine, the speculative search for items that satisfy said updated not-yet-submitted search query prior to receiving, from said client node, an indication that said updated not-yet-submitted search query is completely formed; providing, from the search engine, to said client node, information about at least one item, found by the speculative search, that satisfies said updated not-yet submitted search query; wherein the at least one item, found by the speculative search, includes at least one of (a) a web page, (b) a graphic, or (c) textual information; wherein the method is performed by one or more computing devices. 9. The method of claim 1 , further comprising: storing a plurality of fallback search results from a plurality of previous searches that were performed prior to receiving the updated not-yet-submitted search query; wherein determining said speculative search result comprises: identifying one or more search results based on the updated not-yet-submitted search query; determining that the one or more search results do not exceed a relevancy threshold; in response to determining that determining that the one or more search results do not exceed the relevancy threshold, identifying one or more fallback search results of the plurality of fallback search results; wherein the one or more fallback search results includes the speculative search result. | 0.5 |
8,433,997 | 1 | 3 | 1. A computer-implemented method for formatting electronic documents, comprising: receiving, at a computer system, a source version of an electronic document in a source format, where one or more Type 1 fonts are embedded in the source version of the electronic document; processing the source version of the electronic document by unifying object duplicates in the electronic document to generate a target version of the electronic document in a target format, the target version of the electronic document in the target format excluding one of each of the object duplicates; and outputting the target version of the electronic document in a target format, wherein unifying object duplicates comprises: detecting content duplication at a sub-object level; extracting duplicated content as individual objects; compressing one or more images embedded in the source version of the electronic document, compressing one or more Type 1 fonts embedded in the source version of the electronic document, and unifying object duplicates embedded in the source version of the electronic document. | 1. A computer-implemented method for formatting electronic documents, comprising: receiving, at a computer system, a source version of an electronic document in a source format, where one or more Type 1 fonts are embedded in the source version of the electronic document; processing the source version of the electronic document by unifying object duplicates in the electronic document to generate a target version of the electronic document in a target format, the target version of the electronic document in the target format excluding one of each of the object duplicates; and outputting the target version of the electronic document in a target format, wherein unifying object duplicates comprises: detecting content duplication at a sub-object level; extracting duplicated content as individual objects; compressing one or more images embedded in the source version of the electronic document, compressing one or more Type 1 fonts embedded in the source version of the electronic document, and unifying object duplicates embedded in the source version of the electronic document. 3. The method of claim 1 , wherein the source format and the target format are both portable document format (PDF). | 0.911674 |
9,037,956 | 23 | 24 | 23. A non-transitory, computer-readable medium storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: from an item of original content comprising a plurality of original objects, selecting an original object, the selected original object comprising original attributes; selecting a replacement object from a source other than the item of original content; replacing, in the item of original content, the original attributes of the selected original object with attributes of the replacement object to form an item of modified content; determining a coherence value for the item of modified content, wherein the coherence value is based at least in part on a number of times that two or more words included in the item of modified content appear together in a plurality of items of original content; and based at least in part on a determination that the coherence value does not satisfy a threshold value, replacing, in the item of modified content, at least one attribute of the selected replacement object with at least one of: a different attribute of the selected replacement object or an attribute of a different replacement object. | 23. A non-transitory, computer-readable medium storing computer executable instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising: from an item of original content comprising a plurality of original objects, selecting an original object, the selected original object comprising original attributes; selecting a replacement object from a source other than the item of original content; replacing, in the item of original content, the original attributes of the selected original object with attributes of the replacement object to form an item of modified content; determining a coherence value for the item of modified content, wherein the coherence value is based at least in part on a number of times that two or more words included in the item of modified content appear together in a plurality of items of original content; and based at least in part on a determination that the coherence value does not satisfy a threshold value, replacing, in the item of modified content, at least one attribute of the selected replacement object with at least one of: a different attribute of the selected replacement object or an attribute of a different replacement object. 24. The non-transitory, computer-readable medium of claim 23 , wherein the item of original content comprises at least one of an electronic book or a computer game. | 0.658333 |
10,067,933 | 2 | 3 | 2. The method of claim 1 , wherein the step of identifying at least one dialect component includes identifying at least two dialect components associated with the language of the text communication is present in the text communication; wherein the areas to which the identified dialect components are associated is at least two areas; and wherein the step of presenting includes presenting representations of the retrieved areas on the display. | 2. The method of claim 1 , wherein the step of identifying at least one dialect component includes identifying at least two dialect components associated with the language of the text communication is present in the text communication; wherein the areas to which the identified dialect components are associated is at least two areas; and wherein the step of presenting includes presenting representations of the retrieved areas on the display. 3. The method of claim 2 wherein the representations are geospatial representations on a map. | 0.5 |
8,364,631 | 1 | 9 | 1. A method of converting a first version of a database to a second version, including: determining the database version of the first version of the database based on a key obtained by taking a hash of the database schema of the database in the first version; determining available translation steps; using a processor to select a translation path from the first version of the database to the second version of the database, wherein the translation path includes a sequence of two or more translation steps that are a subset of the available translation steps, wherein the selected translation steps comprise a first selected translation step and a second selected translation step, each translation step being expressed in a markup language; and executing the selected translation steps in the translation path, wherein executing comprises: using the first selected translation step to convert the first version of the database to a third version of the database; and using the second selected translation step to convert the third version of the database to the second version of the database, wherein each of the first version of the database, the second version of the database, and the third version of the database is associated with a fully migrated version of the database, wherein the selecting of the translation path includes selecting one translation path from a plurality of translation paths based on either a shortest path or a first path, the third version of the database being different from each other for each of the translation paths. | 1. A method of converting a first version of a database to a second version, including: determining the database version of the first version of the database based on a key obtained by taking a hash of the database schema of the database in the first version; determining available translation steps; using a processor to select a translation path from the first version of the database to the second version of the database, wherein the translation path includes a sequence of two or more translation steps that are a subset of the available translation steps, wherein the selected translation steps comprise a first selected translation step and a second selected translation step, each translation step being expressed in a markup language; and executing the selected translation steps in the translation path, wherein executing comprises: using the first selected translation step to convert the first version of the database to a third version of the database; and using the second selected translation step to convert the third version of the database to the second version of the database, wherein each of the first version of the database, the second version of the database, and the third version of the database is associated with a fully migrated version of the database, wherein the selecting of the translation path includes selecting one translation path from a plurality of translation paths based on either a shortest path or a first path, the third version of the database being different from each other for each of the translation paths. 9. A method as recited in claim 1 , wherein executing includes processing an XML representation of each of the sequence of the two or more translation steps. | 0.577957 |
8,892,480 | 10 | 13 | 10. A non-transitory computer-readable storage medium comprising executable computer program code for providing contextual information to a user, the computer program code comprising instructions for: receiving context data describing the user's current context; identifying a plurality of information items corresponding to the user's current context; applying a personalized user behavior model for the user to determine, for each of the plurality of information items, a likelihood that the information item will be of value to the user, the user behavior model including a routine model describing correlations between contexts, the routine model comprising a plurality of transition rules; selecting an information item from among the plurality of information items based on the corresponding likelihood; providing the selected information item for presentation to the user; receiving feedback indicating the user found value in presentation of the selected information item; identifying a contributing transition rule from among the plurality of transition rules based on the contributing transition rule having contributed to selection of the selected information item, the contributing transition rule comprising a source context and a destination context; and storing an indication that the user found value in presentation of the selected information item in conjunction with the contributing transition rule such that information items associated with the destination context are in future determined to have a higher likelihood when the user is associated with the source context. | 10. A non-transitory computer-readable storage medium comprising executable computer program code for providing contextual information to a user, the computer program code comprising instructions for: receiving context data describing the user's current context; identifying a plurality of information items corresponding to the user's current context; applying a personalized user behavior model for the user to determine, for each of the plurality of information items, a likelihood that the information item will be of value to the user, the user behavior model including a routine model describing correlations between contexts, the routine model comprising a plurality of transition rules; selecting an information item from among the plurality of information items based on the corresponding likelihood; providing the selected information item for presentation to the user; receiving feedback indicating the user found value in presentation of the selected information item; identifying a contributing transition rule from among the plurality of transition rules based on the contributing transition rule having contributed to selection of the selected information item, the contributing transition rule comprising a source context and a destination context; and storing an indication that the user found value in presentation of the selected information item in conjunction with the contributing transition rule such that information items associated with the destination context are in future determined to have a higher likelihood when the user is associated with the source context. 13. The non-transitory computer-readable storage medium of claim 10 , wherein identifying a plurality of items corresponding to the user's current context comprises: applying the routine model to the user's current context to predict a likely future location for the user; determining a distance between the likely future location and a location corresponding to a first information item in a corpus; and including the first information item in the plurality of information items responsive to the distance being less than a threshold. | 0.574722 |
7,739,273 | 7 | 9 | 7. A method for searching through a collection of text and active documents used in a support environment, comprising: creating active content comprising a series of questions and answers to resolve a problem via a graphical user interface; storing the active content together with text documents comprising solutions to problems in a storage device comprising a repository; providing a user search query comprising a problem description; searching the text documents and active content to identify answers in response to the user search query, interactively presenting the user with a series of questions and answers in a graphical user interface to resolve a problem in the problem description; bypassing questions that are explicitly or implicitly answered by analysis of the active content in response to the user search query, thereby alleviating the need for the user to answer said questions; presenting a user with questions, a set of answers, and a list of previously-asked questions and their answers in a graphical user interface; allowing the user to change previous answers erroneously presumed to be answered by the search query, recording the user's interactions as a transcript; and copying the transcript into a system or application clipboard. | 7. A method for searching through a collection of text and active documents used in a support environment, comprising: creating active content comprising a series of questions and answers to resolve a problem via a graphical user interface; storing the active content together with text documents comprising solutions to problems in a storage device comprising a repository; providing a user search query comprising a problem description; searching the text documents and active content to identify answers in response to the user search query, interactively presenting the user with a series of questions and answers in a graphical user interface to resolve a problem in the problem description; bypassing questions that are explicitly or implicitly answered by analysis of the active content in response to the user search query, thereby alleviating the need for the user to answer said questions; presenting a user with questions, a set of answers, and a list of previously-asked questions and their answers in a graphical user interface; allowing the user to change previous answers erroneously presumed to be answered by the search query, recording the user's interactions as a transcript; and copying the transcript into a system or application clipboard. 9. A method according to claim 7 , further comprising transferring the transcript into a problem ticketing system. | 0.821875 |
8,576,049 | 1 | 3 | 1. A computer-implemented method for encoding identification information in a document, comprising: receiving a digitized document; creating a set of markers associated with identification information; determining whether permission to modify semantic content of the digitized document is given; selecting an encoding strategy to apply the set of markers to the digitized document as identifying tags to produce an encoded document based on the determination whether permission to modify the semantic content of the digitized document is given, wherein the encoding strategy includes retaining semantic content of the digitized document based on determining that permission to modify the semantic content of the digitized document is not given, and the encoding strategy includes modifying the semantic content of the digitized document based on determining that permission to modify the semantic content of the digitized document is given; and applying the set of markers to the digitized document according to the encoding strategy using an encoder to produce an encoded document with one or more characters added or replaced compared to the digitized document. | 1. A computer-implemented method for encoding identification information in a document, comprising: receiving a digitized document; creating a set of markers associated with identification information; determining whether permission to modify semantic content of the digitized document is given; selecting an encoding strategy to apply the set of markers to the digitized document as identifying tags to produce an encoded document based on the determination whether permission to modify the semantic content of the digitized document is given, wherein the encoding strategy includes retaining semantic content of the digitized document based on determining that permission to modify the semantic content of the digitized document is not given, and the encoding strategy includes modifying the semantic content of the digitized document based on determining that permission to modify the semantic content of the digitized document is given; and applying the set of markers to the digitized document according to the encoding strategy using an encoder to produce an encoded document with one or more characters added or replaced compared to the digitized document. 3. The method of claim 1 wherein when it is determined that permission to modify the semantic content of the digitized document is given, the encoding strategy includes inserting a signature quote into the digitized document, wherein characters, words or phrases in the signature quote equate to the set of markers. | 0.5 |
7,865,394 | 56 | 57 | 56. A system as recited in claim 22 , wherein the delivery routine further comprises a routine for collecting delivery and personal information about an additional recipient wherein the message may be re-individualized and delivered as word-of-mouth style advertising to the additional recipient. | 56. A system as recited in claim 22 , wherein the delivery routine further comprises a routine for collecting delivery and personal information about an additional recipient wherein the message may be re-individualized and delivered as word-of-mouth style advertising to the additional recipient. 57. A system as recited in claim 56 , wherein the routine for collecting the delivery and personal information further collects optional information from the original recipient, such as recommendations, and an improved subject line. | 0.5 |
8,583,460 | 13 | 14 | 13. The method of claim 12 wherein the sellable units are priceable units and associating price information with the context-free grammar further comprises: linking, by one or more computers, the price information with non-terminal priceable-unit symbols in the context free grammar. | 13. The method of claim 12 wherein the sellable units are priceable units and associating price information with the context-free grammar further comprises: linking, by one or more computers, the price information with non-terminal priceable-unit symbols in the context free grammar. 14. The method of claim 13 wherein linking links the price information with the context-free grammar linking fares in the sellable units as a list. | 0.5 |
10,063,582 | 1 | 8 | 1. A computer-implemented method for securing compromised network devices in a network, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: (a) identifying a Positive Unlabeled (PU) machine learning classifier; (b) selecting labeled positive samples and unlabeled positive and negative samples as a bootstrap subset of training data from a set of training data; (c) training the PU machine learning classifier with the bootstrap subset of training data; (d) repeating (a)-(c) one or more times to create a set of trained PU machine learning classifiers; (e) predicting probabilities that a network device in a network has been compromised using each of the trained PU machine learning classifiers in the set of trained PU machine learning classifiers; (f) combining the probabilities predicted at (e) to generate a combined risk score for the network device; (g) repeating (e)-(f) one or more times to create a ranked list of combined risk scores; and (h) performing a security action on one or more of the network devices in the ranked list. | 1. A computer-implemented method for securing compromised network devices in a network, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: (a) identifying a Positive Unlabeled (PU) machine learning classifier; (b) selecting labeled positive samples and unlabeled positive and negative samples as a bootstrap subset of training data from a set of training data; (c) training the PU machine learning classifier with the bootstrap subset of training data; (d) repeating (a)-(c) one or more times to create a set of trained PU machine learning classifiers; (e) predicting probabilities that a network device in a network has been compromised using each of the trained PU machine learning classifiers in the set of trained PU machine learning classifiers; (f) combining the probabilities predicted at (e) to generate a combined risk score for the network device; (g) repeating (e)-(f) one or more times to create a ranked list of combined risk scores; and (h) performing a security action on one or more of the network devices in the ranked list. 8. The method of claim 1 , wherein (h) comprises performing the security action on one or more of the network devices positioned highest in the ranked list. | 0.846154 |
8,117,339 | 1 | 13 | 1. A method for tracking domain name related reputation, comprising the steps of: a) a Registering Entity setting a numeric reputation rating for a domain name to an initial value, wherein said numeric reputation rating is stored in a computer database, and b) said Registering Entity changing said numeric reputation rating for said domain name in said computer database, wherein said numeric reputation rating comprises a value for privacy policies and practices associated with said domain name, wherein said computer database is maintained by said Registering Entity. | 1. A method for tracking domain name related reputation, comprising the steps of: a) a Registering Entity setting a numeric reputation rating for a domain name to an initial value, wherein said numeric reputation rating is stored in a computer database, and b) said Registering Entity changing said numeric reputation rating for said domain name in said computer database, wherein said numeric reputation rating comprises a value for privacy policies and practices associated with said domain name, wherein said computer database is maintained by said Registering Entity. 13. The method of claim 1 , wherein said numeric reputation rating comprises a value for website content associated with said domain name. | 0.638743 |
5,486,646 | 1 | 4 | 1. A rhythm creating system comprising: an input means for entering a plurality of parameters specifying the genre and meter of the rhythm to be created; a memory for storing a rule data base for associating the parameters with rhythm patterns including a LEVEL rule specifying complexity of the desired pattern, and a LENGTH rule specifying a number of bars of a rhythm pattern to be created; and a rhythm pattern creating means for creating desired rhythm patterns by an inference system with reference to the rule data base on the basis of the plurality of parameters entered by the input means. | 1. A rhythm creating system comprising: an input means for entering a plurality of parameters specifying the genre and meter of the rhythm to be created; a memory for storing a rule data base for associating the parameters with rhythm patterns including a LEVEL rule specifying complexity of the desired pattern, and a LENGTH rule specifying a number of bars of a rhythm pattern to be created; and a rhythm pattern creating means for creating desired rhythm patterns by an inference system with reference to the rule data base on the basis of the plurality of parameters entered by the input means. 4. A rhythm creating system according to claim 1, wherein said rhythm pattern creating means is provided with a means for creating new rhythm patterns by operating the created rhythm patterns. | 0.757576 |
9,223,537 | 1 | 7 | 1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors of a virtual assistant service to perform acts comprising: generating data for a conversation graphical user interface (GUI) that represents a virtual assistant; causing display of the conversation GUI via a computing device to enable a conversation between the virtual assistant and a user of the computing device; receiving user input that is provided via the conversation GUI, the user input comprising one of audio input, keypad input, or touch input; parsing the user input with a natural language processing system that employs a language model; determining, with the natural language processing system, a response based at least in part on (i) the parsed user input and (ii) at least one of content of a service provider, content of the virtual assistant service, or content of the computing device of the user; identifying an assumption that is used to determine the response, the assumption comprising at least one of the language model that is employed by the natural language processing system, a profile for the user, or a learned behavior of the user; causing display of a dialog representation in the conversation GUI for the user input; causing display of a dialog representation in the conversation GUI for the response; causing display of the assumption in the conversation GUI; enabling the user to interact with the conversation GUI to modify the assumption; receiving a modification to the assumption; determining a revised response based at least in part on the modification to the assumption; and causing display of the revised response in the conversation GUI. | 1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed on one or more processors, cause the one or more processors of a virtual assistant service to perform acts comprising: generating data for a conversation graphical user interface (GUI) that represents a virtual assistant; causing display of the conversation GUI via a computing device to enable a conversation between the virtual assistant and a user of the computing device; receiving user input that is provided via the conversation GUI, the user input comprising one of audio input, keypad input, or touch input; parsing the user input with a natural language processing system that employs a language model; determining, with the natural language processing system, a response based at least in part on (i) the parsed user input and (ii) at least one of content of a service provider, content of the virtual assistant service, or content of the computing device of the user; identifying an assumption that is used to determine the response, the assumption comprising at least one of the language model that is employed by the natural language processing system, a profile for the user, or a learned behavior of the user; causing display of a dialog representation in the conversation GUI for the user input; causing display of a dialog representation in the conversation GUI for the response; causing display of the assumption in the conversation GUI; enabling the user to interact with the conversation GUI to modify the assumption; receiving a modification to the assumption; determining a revised response based at least in part on the modification to the assumption; and causing display of the revised response in the conversation GUI. 7. One or more non-transitory computer-readable media as recited in claim 1 , wherein the acts further comprise recording modifications made by the user to the assumption and learning from the user modifications to adjust how the determination of the response to a future user input is made. | 0.694969 |
9,922,108 | 15 | 17 | 15. The method of claim 10 , further comprising: providing, by the computer system, a user access to a plurality of remote data sets; and importing, by the computer system, at least a portion of a user selected remote data set. | 15. The method of claim 10 , further comprising: providing, by the computer system, a user access to a plurality of remote data sets; and importing, by the computer system, at least a portion of a user selected remote data set. 17. The method of claim 15 , further comprising receiving, by the computer system, through the graphical user interface, at least one designation of a remote data object within the remote data set; and transforming, by the computer system, at least a portion of the user selected remote data set into the target data set, wherein generating the data transform the system includes generating the data transform according to the at least one designation of the remote data object within the remote data set. | 0.5 |
8,612,230 | 1 | 4 | 1. A method of automatic speech recognition (‘ASR’), the method implemented with a speech recognition grammar of a multimodal application, with the multimodal application operating on a multimodal device supporting multiple modes of user interaction with the multimodal application, the modes of user interaction including a voice mode and a visual mode, the multimodal application operatively coupled to a grammar interpreter and configured to enable a user of the multimodal application to select or deselect multiple items in a selection list using a single utterance, the method comprising: accepting, by the multimodal application, speech input corresponding to the single utterance for selecting or deselecting one or more items in the selection list; providing, from the multimodal application to the grammar interpreter, the speech input and a speech recognition grammar associated with the selection list; receiving, by the multimodal application from the grammar interpreter, interpretation results, the interpretation results including at least one matched word from the grammar that identifies at least one item in the selection list and a separate indication of whether to select or deselect the at least one item in the selection list, wherein the separate indication is based, at least in part, on the speech input; and selecting or deselecting based, at least in part, on the separate indication, the at least one item in the selection list that corresponds to the at least one matched word. | 1. A method of automatic speech recognition (‘ASR’), the method implemented with a speech recognition grammar of a multimodal application, with the multimodal application operating on a multimodal device supporting multiple modes of user interaction with the multimodal application, the modes of user interaction including a voice mode and a visual mode, the multimodal application operatively coupled to a grammar interpreter and configured to enable a user of the multimodal application to select or deselect multiple items in a selection list using a single utterance, the method comprising: accepting, by the multimodal application, speech input corresponding to the single utterance for selecting or deselecting one or more items in the selection list; providing, from the multimodal application to the grammar interpreter, the speech input and a speech recognition grammar associated with the selection list; receiving, by the multimodal application from the grammar interpreter, interpretation results, the interpretation results including at least one matched word from the grammar that identifies at least one item in the selection list and a separate indication of whether to select or deselect the at least one item in the selection list, wherein the separate indication is based, at least in part, on the speech input; and selecting or deselecting based, at least in part, on the separate indication, the at least one item in the selection list that corresponds to the at least one matched word. 4. The method of claim 1 further comprising: establishing in the multimodal device a configuration parameter for the multimodal application, the value of the configuration parameter being user-editable and indicating whether to add to existing item selections items that correspond to the at least one matched word, or to replace existing item selections with items that correspond to the at least one matched word; wherein determining whether to select or deselect items in the selection list that correspond to the at least one matched word further comprises determining whether to select or deselect items in the selection list that correspond to the at least one matched word based, at least in part, on the value of the configuration parameter, regardless of the content of the separate indication of whether to select or deselect the at least one item in the selection list. | 0.5 |
7,512,938 | 11 | 13 | 11. The at least one computer-readable medium of claim 10 , wherein an approximated record type is associated with the type variable with the sub-classing bound. | 11. The at least one computer-readable medium of claim 10 , wherein an approximated record type is associated with the type variable with the sub-classing bound. 13. The at least one computer-readable medium of claim 11 , wherein the approximated record type associated with the type variable with sub-classing bounds comprises one or more virtual methods whose types include the type variable. | 0.539683 |
7,934,264 | 15 | 19 | 15. A system for using metadata to detect alteration of audio data, image data, or both, the system comprising: a metadata recording device capable of recording a first set of metadata characteristics for a first set of data representing original data, the first set of metadata characteristics including at least one respective semantic description for the first set of data; the metadata recording device capable of recording a second set of metadata characteristics for a second set of data representing data under test, the second set of metadata characteristics including at least one corresponding semantic description for the second set of data; and a comparing mechanism, operatively coupled to the metadata recording device, for comparing the first and second sets of metadata characteristics wherein, if the first and second sets of metadata characteristics are not identical, then the second set of data is identified as an altered version of the first set of data; wherein the comparing mechanism identifies the second set of data as an altered version of the first set of data and processes the first and second sets of metadata characteristics to identify one or more of: at least one location in the first set of data that have been altered, or at least one metadata characteristic that has changed from the first set of data to the second set of data using the at least one semantic description for the first set of data and the at least one corresponding semantic description for the second set of data; and wherein the comparing mechanism further identifies at least one metadata characteristic that has changed from the first set of data to the second set of data using the at least one semantic description for the first set of data and the at least one corresponding semantic description for the second set of data, defines a change of semantic meaning between the first set of data and the second set of data using the identified at least one metadata characteristic to determine whether or not the defined change of semantic meaning changes an overall meaning of a message defined by the first set of data and generates an alert indicative of a possible criminal intent in altering the message defined by the first set of data if the defined change of semantic meaning changes an overall meaning of a message defined by the first set of data. | 15. A system for using metadata to detect alteration of audio data, image data, or both, the system comprising: a metadata recording device capable of recording a first set of metadata characteristics for a first set of data representing original data, the first set of metadata characteristics including at least one respective semantic description for the first set of data; the metadata recording device capable of recording a second set of metadata characteristics for a second set of data representing data under test, the second set of metadata characteristics including at least one corresponding semantic description for the second set of data; and a comparing mechanism, operatively coupled to the metadata recording device, for comparing the first and second sets of metadata characteristics wherein, if the first and second sets of metadata characteristics are not identical, then the second set of data is identified as an altered version of the first set of data; wherein the comparing mechanism identifies the second set of data as an altered version of the first set of data and processes the first and second sets of metadata characteristics to identify one or more of: at least one location in the first set of data that have been altered, or at least one metadata characteristic that has changed from the first set of data to the second set of data using the at least one semantic description for the first set of data and the at least one corresponding semantic description for the second set of data; and wherein the comparing mechanism further identifies at least one metadata characteristic that has changed from the first set of data to the second set of data using the at least one semantic description for the first set of data and the at least one corresponding semantic description for the second set of data, defines a change of semantic meaning between the first set of data and the second set of data using the identified at least one metadata characteristic to determine whether or not the defined change of semantic meaning changes an overall meaning of a message defined by the first set of data and generates an alert indicative of a possible criminal intent in altering the message defined by the first set of data if the defined change of semantic meaning changes an overall meaning of a message defined by the first set of data. 19. The system of claim 15 further comprising a labeling mechanism operatively coupled to the metadata recording device for labeling the first set of data with a label indicative of whether or not the first set of metadata was recorded using speech recognition. | 0.7 |
8,133,121 | 9 | 10 | 9. A system for organizing a plurality of electronic game tables, comprising: a game server in communication with a plurality of remote client computers; each of said remote client computers including a display; and at least one display including a console area and a stacking component area, wherein said console area displays an active table, wherein said stacking component area aggregates and displays information about said plurality of electronic game tables, and further wherein said stacking component presents a player with information pertaining to each of said tables and allows said player to select a table, make it an active table, and display said table in said console area. | 9. A system for organizing a plurality of electronic game tables, comprising: a game server in communication with a plurality of remote client computers; each of said remote client computers including a display; and at least one display including a console area and a stacking component area, wherein said console area displays an active table, wherein said stacking component area aggregates and displays information about said plurality of electronic game tables, and further wherein said stacking component presents a player with information pertaining to each of said tables and allows said player to select a table, make it an active table, and display said table in said console area. 10. A system according to claim 9 , said stacking component area comprising: a table list sub-component; and a player information sub-component. | 0.796034 |
8,484,675 | 1 | 2 | 1. A system comprising: a user behavior analyzer to which an end-user can couple, said user behavior analyzer being adapted to analyze the behavior of said end-user with respect to a plurality of content providers adapted to transmit first multimedia data to said end-user via a first content selector controllable by said end-user to transmit the first multimedia data from a selected one of said content providers, wherein said user behavior analyzer is adapted to transmit input controls signals to an opportunities selector, adapted (i) to receive additional input control signals from a plurality of additional content providers adapted to transmit second multimedia data to said end-user via a second content selector and (ii) to cause said second content selector to transmit to said end-user said second multimedia data from a selected one of said additional content providers; and a multimedia data combiner adapted to combine said second multimedia data with the first multimedia data into a combined multimedia data signal and to transmit said combined multimedia data signal to said end-user. | 1. A system comprising: a user behavior analyzer to which an end-user can couple, said user behavior analyzer being adapted to analyze the behavior of said end-user with respect to a plurality of content providers adapted to transmit first multimedia data to said end-user via a first content selector controllable by said end-user to transmit the first multimedia data from a selected one of said content providers, wherein said user behavior analyzer is adapted to transmit input controls signals to an opportunities selector, adapted (i) to receive additional input control signals from a plurality of additional content providers adapted to transmit second multimedia data to said end-user via a second content selector and (ii) to cause said second content selector to transmit to said end-user said second multimedia data from a selected one of said additional content providers; and a multimedia data combiner adapted to combine said second multimedia data with the first multimedia data into a combined multimedia data signal and to transmit said combined multimedia data signal to said end-user. 2. The system according to claim 1 , wherein said opportunities selector is adapted to receive the additional input control signals that contain information about multimedia data available for transmission to said end user from said additional content providers. | 0.797214 |
8,712,761 | 3 | 4 | 3. The method of claim 2 , further comprising providing, for display at the user device, an environment that includes a first portion that displays the display message template, including the one or more template placeholders, and a second portion that receives text input from the user, wherein the user enters the translated text portion at the second portion. | 3. The method of claim 2 , further comprising providing, for display at the user device, an environment that includes a first portion that displays the display message template, including the one or more template placeholders, and a second portion that receives text input from the user, wherein the user enters the translated text portion at the second portion. 4. The method of claim 3 , further comprising allowing the user to slide the one or more non-editable objects from the display message template in the first portion into the translated text portion entered by the user in the second portion to create the translated display message template. | 0.60274 |
9,654,521 | 1 | 2 | 1. A method of providing a time data capsule for a designated time period in an electronic conference, comprising: identifying meeting input sources related to the electronic conference, wherein the meeting input sources for the electronic conference comprise a raising hand input source, a response to polling question input source, a slide presentation input source, and a show desktop input source; capturing respective data of the meeting input sources from a start through an end of the electronic conference, at least one of maintaining and creating timestamps for the respective data that is captured, the timestamps associated with the respective data; aligning by a computer the respective data of the meeting input sources along a timeline according to the timestamps respectively associated with the respective data from the meeting input sources; converting the respective data of the meeting input sources into at least one of text equivalents and text descriptions with the timestamps retained; identifying a trigger during the electronic conference that causes a timebox to be created, the timebox having a beginning time and an ending time; wherein criteria for the trigger is set in advance; and wherein options of the criteria for the trigger comprise a positive emotion of a given participant, a negative emotion of the given participant, a problem verbally indicated by the given participant during the electronic conference, an interaction during the electronic conference by a person having a pre-identified role or name, and a positive commitment indicated during the electronic conference to take action by the given participant for a product order; extracting the respective data of the meeting input sources for the timestamps that correspond to the timebox resulting in extracted respective data; and creating a package comprising the extracted respective data from the meeting input sources in which the extracted respective data occurred during the timebox initiated by the trigger. | 1. A method of providing a time data capsule for a designated time period in an electronic conference, comprising: identifying meeting input sources related to the electronic conference, wherein the meeting input sources for the electronic conference comprise a raising hand input source, a response to polling question input source, a slide presentation input source, and a show desktop input source; capturing respective data of the meeting input sources from a start through an end of the electronic conference, at least one of maintaining and creating timestamps for the respective data that is captured, the timestamps associated with the respective data; aligning by a computer the respective data of the meeting input sources along a timeline according to the timestamps respectively associated with the respective data from the meeting input sources; converting the respective data of the meeting input sources into at least one of text equivalents and text descriptions with the timestamps retained; identifying a trigger during the electronic conference that causes a timebox to be created, the timebox having a beginning time and an ending time; wherein criteria for the trigger is set in advance; and wherein options of the criteria for the trigger comprise a positive emotion of a given participant, a negative emotion of the given participant, a problem verbally indicated by the given participant during the electronic conference, an interaction during the electronic conference by a person having a pre-identified role or name, and a positive commitment indicated during the electronic conference to take action by the given participant for a product order; extracting the respective data of the meeting input sources for the timestamps that correspond to the timebox resulting in extracted respective data; and creating a package comprising the extracted respective data from the meeting input sources in which the extracted respective data occurred during the timebox initiated by the trigger. 2. The method of claim 1 , further comprising parsing the extracted respective data in the timebox to determine keywords for searching. | 0.84763 |
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